SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications

Unsupervised learning for long-term autonomy

This thesis investigates methods to enable a robot to build and maintain an environment model in an automatic manner. Such capabilities are especially important in long-term autonomy, where robots operate for extended periods of time without human intervention. In such scenarios we can no longer assume that the environment and the models will remain static. Rather changes are expected and the robot needs to adapt to the new, unseen, circumstances automatically. The approach described in this thesis is based on clustering the robot’s sensing information. This provides a compact representation of the data which can be updated as more information becomes available. The work builds on affinity propagation (Frey and Dueck, 2007), a recent clustering method which obtains high quality clusters while only requiring similarities between pairs of points, and importantly, selecting the number of clusters automatically. This is essential for real autonomy as we typically do not know “a priori” how many clusters best represent the data. The contributions of this thesis a three fold. First a self-supervised method capable of learning a visual appearance model in long-term autonomy settings is presented. Secondly, affinity propagation is extended to handle multiple sensor modalities, often occurring in robotics, in a principle way. Third, a method for joint clustering and outlier selection is proposed which selects a user defined number of outlier while clustering the data. This is solved using an extension of affinity propagation as well as a Lagrangian duality approach which provides guarantees on the optimality of the solution

Adaptive obstacle detection for mobile robots in urban environments using downward-looking 2D LiDAR

Environment perception is important for collision-free motion planning of outdoor mobile robots. This paper presents an adaptive obstacle detection method for outdoor mobile robots using a single downward-looking LiDAR sensor. The method begins by extracting line segments from the raw sensor data, and then estimates the height and the vector of the scanned road surface at each moment. Subsequently, the segments are divided into either road ground or obstacles based on the average height of each line segment and the deviation between the line segment and the road vector estimated from the previous measurements. A series of experiments have been conducted in several scenarios, including normal scenes and complex scenes. The experimental results show that the proposed approach can accurately detect obstacles on roads and could effectively deal with the different heights of obstacles in urban road environments

Contributions to Intelligent Scene Understanding of Unstructured Environments from 3D lidar sensors

Además, la viabilidad de este enfoque es evaluado mediante la implementación de cuatro tipos de clasificadores de aprendizaje supervisado encontrados en métodos de procesamiento de escenas: red neuronal, máquina de vectores de soporte, procesos gaussianos, y modelos de mezcla gaussiana. La segmentación de objetos es un paso más allá hacia el entendimiento de escena, donde conjuntos de puntos 3D correspondientes al suelo y otros objetos de la escena son aislados. La tesis propone nuevas contribuciones a la segmentación de nubes de puntos basados en mapas de vóxeles caracterizados geométricamente. En concreto, la metodología propuesta se compone de dos pasos: primero, una segmentación del suelo especialmente diseñado para entornos naturales; y segundo, el posterior aislamiento de objetos individuales. Además, el método de segmentación del suelo es integrado en una nueva técnica de mapa de navegabilidad basado en cuadrícula de ocupación el cuál puede ser apropiado para robots móviles en entornos naturales. El diseño y desarrollo de un nuevo y asequible sensor lidar 3D de alta resolución también se ha propuesto en la tesis. Los nuevos MBLs, tales como los desarrollados por Velodyne, están siendo cada vez más un tipo de sensor 3D asequible y popular que ofrece alto ratio de datos en un campo de visión vertical (FOV) limitado. El diseño propuesto consiste en una plataforma giratoria que mejora la resolución y el FOV vertical de un Velodyne VLP-16 de 16 haces. Además, los complejos patrones de escaneo producidos por configuraciones de MBL que rotan se analizan tanto en simulaciones de esfera hueca como en escáneres reales en entornos representativos. Fecha de Lectura de Tesis: 11 de julio 2018.Ingeniería de Sistemas y Automática Resumen tesis: Los sensores lidar 3D son una tecnología clave para navegación, localización, mapeo y entendimiento de escenas en vehículos no tripulados y robots móviles. Esta tecnología, que provee nubes de puntos densas, puede ser especialmente adecuada para nuevas aplicaciones en entornos naturales o desestructurados, tales como búsqueda y rescate, exploración planetaria, agricultura, o exploración fuera de carretera. Esto es un desafío como área de investigación que incluye disciplinas que van desde el diseño de sensor a la inteligencia artificial o el aprendizaje automático (machine learning). En este contexto, esta tesis propone contribuciones al entendimiento inteligente de escenas en entornos desestructurados basado en medidas 3D de distancia a nivel del suelo. En concreto, las contribuciones principales incluyen nuevas metodologías para la clasificación de características espaciales, segmentación de objetos, y evaluación de navegabilidad en entornos naturales y urbanos, y también el diseño y desarrollo de un nuevo lidar rotatorio multi-haz (MBL). La clasificación de características espaciales es muy relevante porque es extensamente requerida como un paso fundamental previo a los problemas de entendimiento de alto nivel de una escena. Las contribuciones de la tesis en este respecto tratan de mejorar la eficacia, tanto en carga computacional como en precisión, de clasificación de aprendizaje supervisado de características de forma espacial (forma tubular, plana o difusa) obtenida mediante el análisis de componentes principales (PCA). Esto se ha conseguido proponiendo un concepto eficiente de vecindario basado en vóxel en una contribución original que define los procedimientos de aprendizaje “offline” y clasificación “online” a la vez que cinco definiciones alternativas de vectores de características basados en PCA

Depth Estimation Using 2D RGB Images

Single image depth estimation is an ill-posed problem. That is, it is not mathematically possible to uniquely estimate the 3rd dimension (or depth) from a single 2D image. Hence, additional constraints need to be incorporated in order to regulate the solution space. As a result, in the first part of this dissertation, the idea of constraining the model for more accurate depth estimation by taking advantage of the similarity between the RGB image and the corresponding depth map at the geometric edges of the 3D scene is explored. Although deep learning based methods are very successful in computer vision and handle noise very well, they suffer from poor generalization when the test and train distributions are not close. While, the geometric methods do not have the generalization problem since they benefit from temporal information in an unsupervised manner. They are sensitive to noise, though. At the same time, explicitly modeling of a dynamic scenes as well as flexible objects in traditional computer vision methods is a big challenge. Considering the advantages and disadvantages of each approach, a hybrid method, which benefits from both, is proposed here by extending traditional geometric models’ abilities to handle flexible and dynamic objects in the scene. This is made possible by relaxing geometric computer vision rules from one motion model for some areas of the scene into one for every pixel in the scene. This enables the model to detect even small, flexible, floating debris in a dynamic scene. However, it makes the optimization under-constrained. To change the optimization from under-constrained to over-constrained while maintaining the model’s flexibility, ”moving object detection loss” and ”synchrony loss” are designed. The algorithm is trained in an unsupervised fashion. The primary results are in no way comparable to the current state of the art. Because the training process is so slow, it is difficult to compare it to the current state of the art. Also, the algorithm lacks stability. In addition, the optical flow model is extremely noisy and naive. At the end, some solutions are suggested to address these issues

Multi-sensor based object detection in driving scenes

The work done in this internship consists in two main part. The first part is the design of an experimental platform to acquire data for testing and training. To design the experiments, onboard and onroad sensors have been considered. A calibration process has been conducted in order to integrated all the data from different sources. The second part was the use of a stereo system and a laser scanner to extract the free navigable space and to detect obstacles. This has been conducted through the use of an occupancy grid map representation

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system