Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

주행계 및 지도 작성을 위한 3차원 확률적 정규분포변환의 정합 방법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 이범희.로봇은 거리센서를 이용하여 위치한 환경의 공간 정보를 점군(point set) 형태로 수집할 수 있는데, 이렇게 수집한 정보를 환경의 복원에 이용할 수 있다. 또한, 로봇은 점군과 모델을 정합하는 위치를 추정할 수 있다. 거리센서가 수집한 점군이 2차원에서 3차원으로 확장되고 해상도가 높아지면서 점의 개수가 크게 증가하면서, NDT (normal distributions transform)를 이용한 정합이 ICP (iterative closest point)의 대안으로 부상하였다. NDT는 점군을 분포로 변환하여 공간을 표현하는 압축된 공간 표현 방법이다. 분포의 개수가 점의 개수에 비해 월등히 작기 때문에 ICP에 비해 빠른 성능을 가졌다. 그러나 NDT 정합 기반 위치 추정의 성능을 좌우하는 셀의 크기, 셀의 중첩 정도, 셀의 방향, 분포의 스케일, 대응쌍의 비중 등 파라미터를 설정하기가 매우 어렵다. 본 학위 논문에서는 이러한 어려움에 대응하여 NDT 정합 기반 위치 추정의 정확도를 향상할 수 있는 방법을 제안하였다. 본 논문은 표현법과 정합법 2개 파트로 나눌 수 있다. 표현법에 있어 본 논문은 다음 3개 방법을 제안하였다. 첫째, 본 논문에서는 분포의 퇴화를 막기 위해 경험적으로 공분산 행렬의 고유값을 수정하여 공간적 형태의 왜곡을 가져오는 문제점과 고해상도의 NDT를 생성할 때 셀당 점의 개수가 감소하며 구조를 반영하는 분포가 형성되지 않는 문제점을 주목했다. 이를 해결하기 위하여 각 점에 대해 불확실성을 부여하고, 평균과 분산의 기대값으로 수정한 확률적 NDT (PNDT, probabilistic NDT) 표현법을 제안하였다. 공간 정보의 누락 없이 모든 점을 분포로 변환한 NDT를 통해 향상된 정확도를 보인 PNDT는 샘플링을 통한 가을을 가능하도록 하였다. 둘째, 본 논문에서는 정육면체를 셀로 다루며, 셀을 중심좌표와 변의 길이로 정의한다. 또한, 셀들로 이뤄진 격자를 각 셀의 중심점 사이의 간격과 셀의 크기로 정의한다. 이러한 정의를 토대로, 본 논문에서는 셀의 확대를 통하여 셀을 중첩시키는 방법과 셀의 간격 조절을 통하여 셀을 중첩시키는 방법을 제안하였다. 본 논문은 기존 2D NDT에서 사용한 셀의 삽입법을 주목하였다. 단순입방구조를 이루는 기존 방법 외에 면심입방구조와 체심입방구조의 셀로 이뤄진 격자가 생성하였다. 그 다음 해당 격자를 이용하여 NDT를 생성하는 방법을 제안하였다. 또한, 이렇게 생성된 NDT를 정합할 때 많은 시간을 소요하기 때문에 대응쌍 검색 영역을 정의하여 정합 속도를 향상하였다. 셋째, 저사양 로봇들은 점군 지도를 NDT 지도로 압축하여 보관하는 것이 효율적이다. 그러나 로봇 포즈가 갱신되거나, 다개체 로봇간 랑데뷰가 일어나 지도를 공유 및 결합하는 경우 NDT의 분포 형태가 왜곡되는 문제가 발생한다. 이러한 문제를 해결하기 위하여 NDT 재생성 방법을 제안하였다. 정합법에 있어 본 논문은 다음 4개 방법을 제안하였다. 첫째, 점군의 각 점에 대해 대응되는 색상 정보가 제공될 때 색상 hue를 이용한 향상된 NDT 정합으로 각 대응쌍에 대해 hue의 유사도를 비중으로 사용하는 목적함수를 제안하였다. 둘째, 본 논문은은 다양한 크기의 위치 변화량에 대응하기 위한 다중 레이어 NDT 정합 (ML-NDT, multi-layered NDT)의 한계를 극복하기 위하여 키레이어 NDT 정합 (KL-NDT, key-layered NDT)을 제안하였다. KL-NDT는 각 해상도의 셀에서 활성화된 점의 개수 변화량을 척도로 키레이어를 결정한다. 또한 키레이어에서 위치의 추정값이 수렴할 때까지 정합을 수행하는 방식을 취하여 다음 키레이어에 더 좋은 초기값을 제공한다. 셋째, 본 논문은 이산적인 셀로 인해 NDT간 정합 기법인 NDT-D2D (distribution-to-distribution NDT)의 목적 함수가 비선형이며 국소 최저치의 완화를 위한 방법으로 신규 NDT와 모델 NDT에 독립된 스케일을 정의하고 스케일을 변화하며 정합하는 동적 스케일 기반 NDT 정합 (DSF-NDT-D2D, dynamic scaling factor-based NDT-D2D)을 제안하였다. 마지막으로, 본 논문은 소스 NDT와 지도간 증대적 정합을 이용한 주행계 추정 및 지도 작성 방법을 제안하였다. 이 방법은 로봇의 현재 포즈에 대한 초기값을 소스 점군에 적용한 뒤 NDT로 변환하여 지도 상 NDT와 가능한 한 유사한 NDT를 작성한다. 그 다음 로봇 포즈 및 소스 NDT의 GC (Gaussian component)를 고려하여 부분지도를 추출한다. 이렇게 추출한 부분지도와 소스 NDT는 다중 레이어 NDT 정합을 수행하여 정확한 주행계를 추정하고, 추정 포즈로 소스 점군을 회전 및 이동 후 기존 지도를 갱신한다. 이러한 과정을 통해 이 방법은 현재 최고 성능을 가진 LOAM (lidar odometry and mapping)에 비하여 더 높은 정확도와 더 빠른 처리속도를 보였다.The robot is a self-operating device using its intelligence, and autonomous navigation is a critical form of intelligence for a robot. This dissertation focuses on localization and mapping using a 3D range sensor for autonomous navigation. The robot can collect spatial information from the environment using a range sensor. This information can be used to reconstruct the environment. Additionally, the robot can estimate pose variations by registering the source point set with the model. Given that the point set collected by the sensor is expanded in three dimensions and becomes dense, registration using the normal distribution transform (NDT) has emerged as an alternative to the most commonly used iterative closest point (ICP) method. NDT is a compact representation which describes using a set of GCs (GC) converted from a point set. Because the number of GCs is much smaller than the number of points, with regard to the computation time, NDT outperforms ICP. However, the NDT has issues to be resolved, such as the discretization of the point set and the objective function. This dissertation is divided into two parts: representation and registration. For the representation part, first we present the probabilistic NDT (PNDT) to deal with the destruction and degeneration problems caused by the small cell size and the sparse point set. PNDT assigns an uncertainty to each point sample to convert a point set with fewer than four points into a distribution. As a result, PNDT allows for more precise registration using small cells. Second, we present lattice adjustment and cell insertion methods to overlap cells to overcome the discreteness problem of the NDT. In the lattice adjustment method, a lattice is expressed as the distance between the cells and the side length of each cell. In the cell insertion method, simple, face-centered-cubic, and body-centered-cubic lattices are compared. Third, we present a means of regenerating the NDT for the target lattice. A single robot updates its poses using simultaneous localization and mapping (SLAM) and fuses the NDT at each pose to update its NDT map. Moreover, multiple robots share NDT maps built with inconsistent lattices and fuse the maps. Because the simple fusion of the NDT maps can change the centers, shapes, and normal vectors of GCs, the regeneration method subdivides the NDT into truncated GCs using the target lattice and regenerates the NDT. For the registration part, first we present a hue-assisted NDT registration if the robot acquires color information corresponding to each point sample from a vision sensor. Each GC of the NDT has a distribution of the hue and uses the similarity of the hue distributions as the weight in the objective function. Second, we present a key-layered NDT registration (KL-NDT) method. The multi-layered NDT registration (ML-NDT) registers points to the NDT in multiple resolutions of lattices. However, the initial cell size and the number of layers are difficult to determine. KL-NDT determines the key layers in which the registration is performed based on the change of the number of activated points. Third, we present a method involving dynamic scaling factors of the covariance. This method scales the source NDT at zero initially to avoid a negative correlation between the likelihood and rotational alignment. It also scales the target NDT from the maximum scale to the minimum scale. Finally, we present a method of incremental registration of PNDTs which outperforms the state-of-the-art lidar odometry and mapping method.1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.1 Point Set Registration . . . . . . . . . . . . . . . . . . . . . 7 1.3.2 Incremental Registration for Odometry Estimation . . . . . . 16 1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.5 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2 Preliminaries 21 2.1 NDT Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 NDT Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 NDT Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Transformation Matrix and The Parameter Vector . . . . . . . . . . . 27 2.5 Cubic Cell and Lattice . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.6 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.7 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.8 Evaluation of Registration . . . . . . . . . . . . . . . . . . . . . . . 31 2.9 Benchmark Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3 Probabilistic NDT Representation 34 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2 Uncertainty of Point Based on Sensor Model . . . . . . . . . . . . . . 36 3.3 Probabilistic NDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4 Generalization of NDT Registration Based on PNDT . . . . . . . . . 40 3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.5.2 Evaluation of Representation . . . . . . . . . . . . . . . . . . 41 3.5.3 Evaluation of Registration . . . . . . . . . . . . . . . . . . . 46 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4 Interpolation for NDT Using Overlapped Regular Cells 51 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2 Lattice Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.3 Crystalline NDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.4.1 Lattice Adjustment . . . . . . . . . . . . . . . . . . . . . . . 56 4.4.2 Performance of Crystalline NDT . . . . . . . . . . . . . . . . 60 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5 Regeneration of Normal Distributions Transform 65 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.2 Mathematical Preliminaries . . . . . . . . . . . . . . . . . . . . . . . 67 5.2.1 Trivariate Normal Distribution . . . . . . . . . . . . . . . . . 67 5.2.2 Truncated Trivariate Normal Distribution . . . . . . . . . . . 67 5.3 Regeneration of NDT . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3.1 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3.2 Subdivision of Gaussian Components . . . . . . . . . . . . . 70 5.3.3 Fusion of Gaussian Components . . . . . . . . . . . . . . . . 72 5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.4.1 Evaluation Metrics for Representation . . . . . . . . . . . . . 73 5.4.2 Representation Performance of the Regenerated NDT . . . . . 75 5.4.3 Computation Performance of the Regeneration . . . . . . . . 82 5.4.4 Application of Map Fusion . . . . . . . . . . . . . . . . . . . 83 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6 Hue-Assisted Registration 91 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6.2 Preliminary of the HSV Model . . . . . . . . . . . . . . . . . . . . . 92 6.3 Colored Octree for Subdivision . . . . . . . . . . . . . . . . . . . . . 94 6.4 HA-NDT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6.5.1 Evaluation of HA-NDT against nhue . . . . . . . . . . . . . . 97 6.5.2 Evaluation of NDT and HA-NDT . . . . . . . . . . . . . . . 98 6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7 Key-Layered NDT Registration 103 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.2 Key-layered NDT-P2D . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 7.3.1 Evaluation of KL-NDT-P2D and ML-NDT-P2D . . . . . . . . 108 7.3.2 Evaluation of KL-NDT-D2D and ML-NDT-D2D . . . . . . . 111 7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 8 Scaled NDT and The Multi-scale Registration 113 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 8.2 Scaled NDT representation and L2 distance . . . . . . . . . . . . . . 114 8.3 NDT-D2D with dynamic scaling factors of covariances . . . . . . . . 116 8.4 Range of scaling factors . . . . . . . . . . . . . . . . . . . . . . . . . 120 8.5 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 8.5.1 Evaluation of the presented method without initial guess . . . 122 8.5.2 Application of odometry estimation . . . . . . . . . . . . . . 125 8.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 9 Scan-to-map Registration 129 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 9.2 Multi-layered PNDT . . . . . . . . . . . . . . . . . . . . . . . . . . 130 9.3 NDT Incremental Registration . . . . . . . . . . . . . . . . . . . . . 132 9.3.1 Initialization of PNDT-Map . . . . . . . . . . . . . . . . . . 133 9.3.2 Generation of Source ML-PNDT . . . . . . . . . . . . . . . . 134 9.3.3 Reconstruction of The Target ML-PNDT . . . . . . . . . . . 134 9.3.4 Pose Estimation Based on Multi-layered Registration . . . . . 135 9.3.5 Update of PNDT-Map . . . . . . . . . . . . . . . . . . . . . 136 9.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 10 Conclusions 142 Bibliography 145 초록 159 감사의 글 162Docto

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models

Intelligent collision avoidance system for industrial manipulators

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe new paradigm of Industry 4.0 demand the collaboration between robot and humans. They could help (human and robot) and collaborate each other without any additional security, unlike other conventional manipulators. For this, the robot should have the ability of acquire the environment and plan (or re-plan) on-the-fly the movement avoiding the obstacles and people. This work proposes a system that acquires the space of the environment, based on a Kinect sensor, verifies the free spaces generated by a Point Cloud and executes the trajectory of manipulators in these free spaces. The simulation system should perform the path planning of a UR5 manipulator for pick-and-place tasks, while avoiding the objects around it, based on the point cloud from Kinect. And due to the results obtained in the simulation, it was possible to apply this system in real situations. The basic structure of the system is the ROS software, which facilitates robotic applications with a powerful set of libraries and tools. The MoveIt! and Rviz are examples of these tools, with them it was possible to carry out simulations and obtain planning results. The results are reported through logs files, indicating whether the robot motion plain was successful and how many manipulator poses were needed to create the final movement. This last step, allows to validate the proposed system, through the use of the RRT and PRM algorithms. Which were chosen because they are most used in the field of robot path planning.Os novos paradigmas da Indústria 4.0 exigem a colaboração entre robôs e seres humanos. Estes podem ajudar e colaborar entre si sem qualquer segurança adicional, ao contrário de outros manipuladores convencionais. Para isto, o robô deve ter a capacidade de adquirir o meio ambiente e planear (ou re-planear) on-the-fly o movimento evitando obstáculos e pessoas. Este trabalho propõe um sistema que adquire o espaço do ambiente através do sensor Kinect. O sistema deve executar o planeamento do caminho de manipuladores que possuem movimentos de um ponto a outro (ponto inicial e final), evitando os objetos ao seu redor, com base na nuvem de pontos gerada pelo Kinect. E devido aos resultados obtidos na simulação, foi possível aplicar este sistema em situações reais. A estrutura base do sistema é o software ROS, que facilita aplicações robóticas com um poderoso conjunto de bibliotecas e ferramentas. O MoveIt! e Rviz são exemplos destas ferramentas, com elas foi possível realizar simulações e conseguir os resultados de planeamento livre de colisões. Os resultados são informados por meio de arquivos logs, indicando se o movimento do UR5 foi realizado com sucesso e quantas poses do manipulador foram necessárias criar para atingir o movimento final. Este último passo, permite validar o sistema proposto, através do uso dos algoritmos RRT e PRM. Que foram escolhidos por serem mais utilizados no ramo de planeamento de trajetória para robôs

Efficient Dense Registration, Segmentation, and Modeling Methods for RGB-D Environment Perception

One perspective for artificial intelligence research is to build machines that perform tasks autonomously in our complex everyday environments. This setting poses challenges to the development of perception skills: A robot should be able to perceive its location and objects in its surrounding, while the objects and the robot itself could also be moving. Objects may not only be composed of rigid parts, but could be non-rigidly deformable or appear in a variety of similar shapes. Furthermore, it could be relevant to the task to observe object semantics. For a robot acting fluently and immediately, these perception challenges demand efficient methods. This theses presents novel approaches to robot perception with RGB-D sensors. It develops efficient registration, segmentation, and modeling methods for scene and object perception. We propose multi-resolution surfel maps as a concise representation for RGB-D measurements. We develop probabilistic registration methods that handle rigid scenes, scenes with multiple rigid parts that move differently, and scenes that undergo non-rigid deformations. We use these methods to learn and perceive 3D models of scenes and objects in both static and dynamic environments. For learning models of static scenes, we propose a real-time capable simultaneous localization and mapping approach. It aligns key views in RGB-D video using our rigid registration method and optimizes the pose graph of the key views. The acquired models are then perceived in live images through detection and tracking within a Bayesian filtering framework. An assumption frequently made for environment mapping is that the observed scene remains static during the mapping process. Through rigid multi-body registration, we take advantage of releasing this assumption: Our registration method segments views into parts that move independently between the views and simultaneously estimates their motion. Within simultaneous motion segmentation, localization, and mapping, we separate scenes into objects by their motion. Our approach acquires 3D models of objects and concurrently infers hierarchical part relations between them using probabilistic reasoning. It can be applied for interactive learning of objects and their part decomposition. Endowing robots with manipulation skills for a large variety of objects is a tedious endeavor if the skill is programmed for every instance of an object class. Furthermore, slight deformations of an instance could not be handled by an inflexible program. Deformable registration is useful to perceive such shape variations, e.g., between specific instances of a tool. We develop an efficient deformable registration method and apply it for the transfer of robot manipulation skills between varying object instances. On the object-class level, we segment images using random decision forest classifiers in real-time. The probabilistic labelings of individual images are fused in 3D semantic maps within a Bayesian framework. We combine our object-class segmentation method with simultaneous localization and mapping to achieve online semantic mapping in real-time. The methods developed in this thesis are evaluated in experiments on publicly available benchmark datasets and novel own datasets. We publicly demonstrate several of our perception approaches within integrated robot systems in the mobile manipulation context.Effiziente Dichte Registrierungs-, Segmentierungs- und Modellierungsmethoden für die RGB-D Umgebungswahrnehmung In dieser Arbeit beschäftigen wir uns mit Herausforderungen der visuellen Wahrnehmung für intelligente Roboter in Alltagsumgebungen. Solche Roboter sollen sich selbst in ihrer Umgebung zurechtfinden, und Wissen über den Verbleib von Objekten erwerben können. Die Schwierigkeit dieser Aufgaben erhöht sich in dynamischen Umgebungen, in denen ein Roboter die Bewegung einzelner Teile differenzieren und auch wahrnehmen muss, wie sich diese Teile bewegen. Bewegt sich ein Roboter selbständig in dieser Umgebung, muss er auch seine eigene Bewegung von der Veränderung der Umgebung unterscheiden. Szenen können sich aber nicht nur durch die Bewegung starrer Teile verändern. Auch die Teile selbst können ihre Form in nicht-rigider Weise ändern. Eine weitere Herausforderung stellt die semantische Interpretation von Szenengeometrie und -aussehen dar. Damit intelligente Roboter unmittelbar und flüssig handeln können, sind effiziente Algorithmen für diese Wahrnehmungsprobleme erforderlich. Im ersten Teil dieser Arbeit entwickeln wir effiziente Methoden zur Repräsentation und Registrierung von RGB-D Messungen. Zunächst stellen wir Multi-Resolutions-Oberflächenelement-Karten (engl. multi-resolution surfel maps, MRSMaps) als eine kompakte Repräsentation von RGB-D Messungen vor, die unseren effizienten Registrierungsmethoden zugrunde liegt. Bilder können effizient in dieser Repräsentation aggregiert werde, wobei auch mehrere Bilder aus verschiedenen Blickpunkten integriert werden können, um Modelle von Szenen und Objekte aus vielfältigen Ansichten darzustellen. Für die effiziente, robuste und genaue Registrierung von MRSMaps wird eine Methode vorgestellt, die Rigidheit der betrachteten Szene voraussetzt. Die Registrierung schätzt die Kamerabewegung zwischen den Bildern und gewinnt ihre Effizienz durch die Ausnutzung der kompakten multi-resolutionalen Darstellung der Karten. Die Registrierungsmethode erzielt hohe Bildverarbeitungsraten auf einer CPU. Wir demonstrieren hohe Effizienz, Genauigkeit und Robustheit unserer Methode im Vergleich zum bisherigen Stand der Forschung auf Vergleichsdatensätzen. In einem weiteren Registrierungsansatz lösen wir uns von der Annahme, dass die betrachtete Szene zwischen Bildern statisch ist. Wir erlauben nun, dass sich rigide Teile der Szene bewegen dürfen, und erweitern unser rigides Registrierungsverfahren auf diesen Fall. Unser Ansatz segmentiert das Bild in Bereiche einzelner Teile, die sich unterschiedlich zwischen Bildern bewegen. Wir demonstrieren hohe Segmentierungsgenauigkeit und Genauigkeit in der Bewegungsschätzung unter Echtzeitbedingungen für die Verarbeitung. Schließlich entwickeln wir ein Verfahren für die Wahrnehmung von nicht-rigiden Deformationen zwischen zwei MRSMaps. Auch hier nutzen wir die multi-resolutionale Struktur in den Karten für ein effizientes Registrieren von grob zu fein. Wir schlagen Methoden vor, um aus den geschätzten Deformationen die lokale Bewegung zwischen den Bildern zu berechnen. Wir evaluieren Genauigkeit und Effizienz des Registrierungsverfahrens. Der zweite Teil dieser Arbeit widmet sich der Verwendung unserer Kartenrepräsentation und Registrierungsmethoden für die Wahrnehmung von Szenen und Objekten. Wir verwenden MRSMaps und unsere rigide Registrierungsmethode, um dichte 3D Modelle von Szenen und Objekten zu lernen. Die räumlichen Beziehungen zwischen Schlüsselansichten, die wir durch Registrierung schätzen, werden in einem Simultanen Lokalisierungs- und Kartierungsverfahren (engl. simultaneous localization and mapping, SLAM) gegeneinander abgewogen, um die Blickposen der Schlüsselansichten zu schätzen. Für das Verfolgen der Kamerapose bezüglich der Modelle in Echtzeit, kombinieren wir die Genauigkeit unserer Registrierung mit der Robustheit von Partikelfiltern. Zu Beginn der Posenverfolgung, oder wenn das Objekt aufgrund von Verdeckungen oder extremen Bewegungen nicht weiter verfolgt werden konnte, initialisieren wir das Filter durch Objektdetektion. Anschließend wenden wir unsere erweiterten Registrierungsverfahren für die Wahrnehmung in nicht-rigiden Szenen und für die Übertragung von Objekthandhabungsfähigkeiten von Robotern an. Wir erweitern unseren rigiden Kartierungsansatz auf dynamische Szenen, in denen sich rigide Teile bewegen. Die Bewegungssegmente in Schlüsselansichten werden zueinander in Bezug gesetzt, um Äquivalenz- und Teilebeziehungen von Objekten probabilistisch zu inferieren, denen die Segmente entsprechen. Auch hier liefert unsere Registrierungsmethode die Bewegung der Kamera bezüglich der Objekte, die wir in einem SLAM Verfahren optimieren. Aus diesen Blickposen wiederum können wir die Bewegungssegmente in dichten Objektmodellen vereinen. Objekte einer Klasse teilen oft eine gemeinsame Topologie von funktionalen Elementen, die durch Formkorrespondenzen ermittelt werden kann. Wir verwenden unsere deformierbare Registrierung, um solche Korrespondenzen zu finden und die Handhabung eines Objektes durch einen Roboter auf neue Objektinstanzen derselben Klasse zu übertragen. Schließlich entwickeln wir einen echtzeitfähigen Ansatz, der Kategorien von Objekten in RGB-D Bildern erkennt und segmentiert. Die Segmentierung basiert auf Ensemblen randomisierter Entscheidungsbäume, die Geometrie- und Texturmerkmale zur Klassifikation verwenden. Wir fusionieren Segmentierungen von Einzelbildern einer Szene aus mehreren Ansichten in einer semantischen Objektklassenkarte mit Hilfe unseres SLAM-Verfahrens. Die vorgestellten Methoden werden auf öffentlich verfügbaren Vergleichsdatensätzen und eigenen Datensätzen evaluiert. Einige unserer Ansätze wurden auch in integrierten Robotersystemen für mobile Objekthantierungsaufgaben öffentlich demonstriert. Sie waren ein wichtiger Bestandteil für das Gewinnen der RoboCup-Roboterwettbewerbe in der RoboCup@Home Liga in den Jahren 2011, 2012 und 2013

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models

High-level environment representations for mobile robots

In most robotic applications we are faced with the problem of building a digital representation of the environment that allows the robot to autonomously complete its tasks. This internal representation can be used by the robot to plan a motion trajectory for its mobile base and/or end-effector. For most man-made environments we do not have a digital representation or it is inaccurate. Thus, the robot must have the capability of building it autonomously. This is done by integrating into an internal data structure incoming sensor measurements. For this purpose, a common solution consists in solving the Simultaneous Localization and Mapping (SLAM) problem. The map obtained by solving a SLAM problem is called ``metric'' and it describes the geometric structure of the environment. A metric map is typically made up of low-level primitives (like points or voxels). This means that even though it represents the shape of the objects in the robot workspace it lacks the information of which object a surface belongs to. Having an object-level representation of the environment has the advantage of augmenting the set of possible tasks that a robot may accomplish. To this end, in this thesis we focus on two aspects. We propose a formalism to represent in a uniform manner 3D scenes consisting of different geometric primitives, including points, lines and planes. Consequently, we derive a local registration and a global optimization algorithm that can exploit this representation for robust estimation. Furthermore, we present a Semantic Mapping system capable of building an \textit{object-based} map that can be used for complex task planning and execution. Our system exploits effective reconstruction and recognition techniques that require no a-priori information about the environment and can be used under general conditions

3D Scene Reconstruction with Micro-Aerial Vehicles and Mobile Devices

Scene reconstruction is the process of building an accurate geometric model of one\u27s environment from sensor data. We explore the problem of real-time, large-scale 3D scene reconstruction in indoor environments using small laser range-finders and low-cost RGB-D (color plus depth) cameras. We focus on computationally-constrained platforms such as micro-aerial vehicles (MAVs) and mobile devices. These platforms present a set of fundamental challenges - estimating the state and trajectory of the device as it moves within its environment and utilizing lightweight, dynamic data structures to hold the representation of the reconstructed scene. The system needs to be computationally and memory-efficient, so that it can run in real time, onboard the platform. In this work, we present three scene reconstruction systems. The first system uses a laser range-finder and operates onboard a quadrotor MAV. We address the issues of autonomous control, state estimation, path-planning, and teleoperation. We propose the multi-volume occupancy grid (MVOG) - a novel data structure for building 3D maps from laser data, which provides a compact, probabilistic scene representation. The second system uses an RGB-D camera to recover the 6-DoF trajectory of the platform by aligning sparse features observed in the current RGB-D image against a model of previously seen features. We discuss our work on camera calibration and the depth measurement model. We apply the system onboard an MAV to produce occupancy-based 3D maps, which we utilize for path-planning. Finally, we present our contributions to a scene reconstruction system for mobile devices with built-in depth sensing and motion-tracking capabilities. We demonstrate reconstructing and rendering a global mesh on the fly, using only the mobile device\u27s CPU, in very large (300 square meter) scenes, at a resolutions of 2-3cm. To achieve this, we divide the scene into spatial volumes indexed by a hash map. Each volume contains the truncated signed distance function for that area of space, as well as the mesh segment derived from the distance function. This approach allows us to focus computational and memory resources only in areas of the scene which are currently observed, as well as leverage parallelization techniques for multi-core processing

Robust and Optimal Methods for Geometric Sensor Data Alignment

Geometric sensor data alignment - the problem of finding the rigid transformation that correctly aligns two sets of sensor data without prior knowledge of how the data correspond - is a fundamental task in computer vision and robotics. It is inconvenient then that outliers and non-convexity are inherent to the problem and present significant challenges for alignment algorithms. Outliers are highly prevalent in sets of sensor data, particularly when the sets overlap incompletely. Despite this, many alignment objective functions are not robust to outliers, leading to erroneous alignments. In addition, alignment problems are highly non-convex, a property arising from the objective function and the transformation. While finding a local optimum may not be difficult, finding the global optimum is a hard optimisation problem. These key challenges have not been fully and jointly resolved in the existing literature, and so there is a need for robust and optimal solutions to alignment problems. Hence the objective of this thesis is to develop tractable algorithms for geometric sensor data alignment that are robust to outliers and not susceptible to spurious local optima. This thesis makes several significant contributions to the geometric alignment literature, founded on new insights into robust alignment and the geometry of transformations. Firstly, a novel discriminative sensor data representation is proposed that has better viewpoint invariance than generative models and is time and memory efficient without sacrificing model fidelity. Secondly, a novel local optimisation algorithm is developed for nD-nD geometric alignment under a robust distance measure. It manifests a wider region of convergence and a greater robustness to outliers and sampling artefacts than other local optimisation algorithms. Thirdly, the first optimal solution for 3D-3D geometric alignment with an inherently robust objective function is proposed. It outperforms other geometric alignment algorithms on challenging datasets due to its guaranteed optimality and outlier robustness, and has an efficient parallel implementation. Fourthly, the first optimal solution for 2D-3D geometric alignment with an inherently robust objective function is proposed. It outperforms existing approaches on challenging datasets, reliably finding the global optimum, and has an efficient parallel implementation. Finally, another optimal solution is developed for 2D-3D geometric alignment, using a robust surface alignment measure. Ultimately, robust and optimal methods, such as those in this thesis, are necessary to reliably find accurate solutions to geometric sensor data alignment problems