EKF-based 3D SLAM for Structured Environment Reconstruction

This paper presents the extension and experimental validation of the widely used EKF1-based SLAM2 algorithm to 3D space. It uses planar features extracted probabilistically from dense three-dimensional point clouds generated by a rotating 2D laser scanner. These features are represented in compliance with the Symmetries and Perturbation model (SPmodel) in a stochastic map. As the robot moves, this map is updated incrementally while its pose is tracked by using an Extended Kalman Filter. After showing how three-dimensional data can be generated, the probabilistic feature extraction method is described, capable of robustly extracting (infinite) planes from structured environments. The SLAM algorithm is then used to track a robot moving through an indoor environment and its capabilities in terms of 3D reconstruction are analyzed

An Iterative 3D Registration Algorithm using Random Pair of Plane Patches

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이범희.본 논문은 3차원 공간에서 평면 패치를 기반으로 이미지 정합을 하는 새로운 기법을 제시한다. 정합에 사용되는 대부분의 알고리즘은 특정점 간의 유사도를 이용하여 대응관계를 구하고 이를 기반으로 두 좌표계 사이의 강체변환을 구한다. 그러나 대응관계를 구하는 문제는 특징점에 대한 정보가 부족하거나 이상점이 발생하는 경우 부정확한 결과를 초래할 수 있고 이는 곧 정합의 실패에 영향을 미칠 수 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 평면 패치들의 집합으로 이루어진 두 3차원 좌표계에서 각각 임의의 평면을 추출한 후 거리 제곱 평균 함수의 값을 계산하여 두 좌표계 간의 유사도를 측정한다. 이 과정을 반복적으로 수행하여 정합하고자 하는 프레임을 가장 유사하게 만드는 강체변환을 결정한다. 그 다음 고정된 강체변환에 대하여 거리 제곱 평균 함수의 값을 최소화 시키는 방향으로 평행이동 벡터를 보정하여 정합을 완료한다. 본 논문의 기법은 대응관계를 찾는 데 걸리는 시간을 줄일 수 있고 이상점에 강인하다는 데 의의가 있으며, 이를 시뮬레이션 및 실제 환경에서의 실험을 통해 검증하였다.목차 초록 i 제 1 장 Introduction 1 1.1 Backgrounds and Motivations . . . . . . . . . . . . . . . . . . . . 1 1.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Point-based Registration . . . . . . . . . . . . . . . . . . . 3 1.2.2 Line-based Registration . . . . . . . . . . . . . . . . . . . 3 1.2.3 Plane-based Registration . . . . . . . . . . . . . . . . . . 4 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 제 2 장 Preliminaries 10 2.1 Plane Patch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 Notation for Plane Patch . . . . . . . . . . . . . . . . . . 10 2.1.2 Problem Formulation using Plane Patch . . . . . . . . . . 14 2.2 Quaternion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1 Basis of quaternion . . . . . . . . . . . . . . . . . . . . . . 16 2.3 RANSAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 제 3 장 Proposed Method 21 3.1 Selection of plane patch pair . . . . . . . . . . . . . . . . . . . . . 22 3.2 Evaluation of Rigid Transformation . . . . . . . . . . . . . . . . . 25 3.2.1 Evaluation of Rotation Matrix based on Quaternion . . . 25 3.2.2 Evaluation of Translation Vector based on Moore-Penrose Pseudo Inverse Matrix . . . . . . . . . . . . . . . . . . . . 27 3.3 Transformation of Plane Patches . . . . . . . . . . . . . . . . . . 27 3.4 Mean Square Distance Function . . . . . . . . . . . . . . . . . . . 32 3.5 Selection of Rigid Transformation . . . . . . . . . . . . . . . . . . 37 3.6 Optimization of Translation Vector . . . . . . . . . . . . . . . . . 38 제 4 장 Simulations 40 4.1 Preconditions for the Simulation . . . . . . . . . . . . . . . . . . 40 4.2 Validity of Random Iteration . . . . . . . . . . . . . . . . . . . . 41 4.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . 46 제 5 장 Real Experiments 49 5.1 Environments for the Experiments . . . . . . . . . . . . . . . . . 49 5.2 Extraction of plane patches from point cloud . . . . . . . . . . . 50 5.3 Results of Real Experiments . . . . . . . . . . . . . . . . . . . . . 51 제 6 장 Conclusion 52 참고문헌 53 Abstract 59 감사의 글 61Maste

Plane-based 3D Mapping for Structured Indoor Environment

Three-dimensional (3D) mapping deals with the problem of building a map of the unknown environments explored by a mobile robot. In contrast to 2D maps, 3D maps contain richer information of the visited places. Besides enabling robot navigation in 3D, a 3D map of the robot surroundings could be of great importance for higher-level robotic tasks, like scene interpretation and object interaction or manipulation, as well as for visualization purposes in general, which are required in surveillance, urban search and rescue, surveying, and others. Hence, the goal of this thesis is to develop a system which is capable of reconstructing the surrounding environment of a mobile robot as a three-dimensional map. Microsoft Kinect camera is a novel sensing sensor that captures dense depth images along with RGB images at high frame rate. Recently, it has dominated the stage of 3D robotic sensing, as it is low-cost, low-power. For this work, it is used as the exteroceptive sensor and obtains 3D point clouds of the surrounding environment. Meanwhile, the wheel odometry of the robot is used to initialize the search for correspondences between different observations. As a single 3D point cloud generated by the Microsoft Kinect sensor is composed of many tens of thousands of data points, it is necessary to compress the raw data to process them efficiently. The method chosen in this work is to use a feature-based representation which simplifies the 3D mapping procedure. The chosen features are planar surfaces and orthogonal corners, which is based on the fact that indoor environments are designed such that walls, ground floors, pillars, and other major parts of the building structures can be modeled as planar surface patches, which are parallel or perpendicular to each other. While orthogonal corners are presented as higher features which are more distinguishable in indoor environment. In this thesis, the main idea is to obtain spatial constraints between pairwise frames by building correspondences between the extracted vertical plane features and corner features. A plane matching algorithm is presented that maximizes the similarity metric between a pair of planes within a search space to determine correspondences between planes. The corner matching result is based on the plane matching results. The estimated spatial constraints form the edges of a pose graph, referred to as graph-based SLAM front-end. In order to build a map, however, a robot must be able to recognize places that it has previously visited. Limitations in sensor processing problem, coupled with environmental ambiguity, make this difficult. In this thesis, we describe a loop closure detection algorithm by compressing point clouds into viewpoint feature histograms, inspired by their strong recognition ability. The estimated roto-translation between detected loop frames is added to the graph representing this newly discovered constraint. Due to the estimation errors, the estimated edges form a non-globally consistent trajectory. With the aid of a linear pose graph optimizing algorithm, the most likely configuration of the robot poses can be estimated given the edges of the graph, referred to as SLAM back-end. Finally, the 3D map is retrieved by attaching each acquired point cloud to the corresponding pose estimate. The approach is validated through different experiments with a mobile robot in an indoor environment

Fusion de données multi-capteurs pour la construction incrémentale du modèle tridimensionnel texturé d'un environnement intérieur par un robot mobile

Ce travail traite la Modélisation 3D d'un environnement intérieur par un robot mobile. La principale contribution concerne la construction d'un modèle géométrique hétérogène combinant des amers plans texturés, des lignes 3D et des points d'intérêt. Pour cela, nous devons fusionner des données géométriques et photométriques. Ainsi, nous avons d'abord amélioré la stéréovision, en proposant une approche de la mise en correspondance stéréoscopique par coupure de graphe. Notre contribution réside dans la construction d'un graphe réduit qui a permis d'accélérer la méthode globale et d'obtenir de meilleurs résultats que les méthodes locales. Aussi, pour percevoir l'environnement, le robot est équipé d'un télémètre laser 3D et d'une caméra. Nous proposons une chaîne algorithmique permettant de construire une carte hétérogène, par l'algorithme de Cartographie et Localisation Simultanées (EKF-SLAM). Le placage de la texture sur les facettes planes a permis de solidifier l'association de données.This thesis examines the problem of 3D Modelling of indoor environment by a mobile robot. Our main contribution consists in constructing a heterogeneous geometrical model containing textured planar landmarks, 3D lines and interest points. For that, we must fuse geometrical and photometrical data. Hence, we began by improving the stereo vision algorithm, and proposed a new approach of stereo matching by graph cuts. The most significant contribution is the construction of a reduced graph that allows to accelerate the global method and to provide better results than the local methods. Also, to perceive the environment, the robot is equipped by a 3D laser scanner and by a camera. We proposed an algorithmic chain allowing to incrementally constructing a heterogeneous map, using the algorithm of Simultaneous Localization and Mapping based (EKF-SLAM). Mapping the texture on the planar landmarks makes more robust the phase of data association

Sequential 3D-SLAM for mobile action planning

Abstract — Reliable mapping and self-localization in three dimensions while moving is essential to survey inaccessible work spaces or to inspect technical plants autonomously. Our solution to this 3D SLAM problem is novel in several respects. First, a new rotating laser-scanning setup is presented for acquiring point clouds and reducing them to surface patches in real time. Second, the SLAM algorithms work entirely on highly reduced, attributed surface models and in 3D. Third, we propose a novel system architecture of an Extended Kalman filter (EKF) for 3D position tracking, cooperating with a 3D range image understanding system for matching, aligning, and integrating overlapping range views. The system is demonstrated by an indoor exploration tour