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

Self-Localization of Autonomous Vehicles Using Landmark Object Detection

Fully autonomous vehicles are rapidly approaching realization and concerns regarding their safety and robustness are a prominent obstacle to their integration into society. A bottleneck of an autonomous vehicle’s safety is its ability to self-localize under all types of conditions. Self-localization is critical for the vehicle to route to a destination, for the vehicle itself to be tracked in case of theft, and in determining local traffic laws to operate harmoniously with other traffic and pedestrians. The global positioning system (GPS) is utilized by autonomous vehicles for self-localization, but the availability and reliability of GPS is not guaranteed in all situations, such as when GPS reception is weak or when an adversary is spoofing information. We address this problem by proposing a self-localization method for autonomous vehicles that does not require GPS at localization time. The framework herein describes the representation of a road network as a graph using the data made available by OpenStreetMap, and an encoding of street segments based on detected landmark objects. We derive the current state observer transition function from the resulting graph, and employ localization methods based on set distance and minimum cost paths to determine the most probable location of the vehicle given a sequence of observed landmarks. Through simulating vehicle traveling paths along a road network generated from real data of a region of Washington D.C., we evaluate the performance of our method for varying degrees of landmark observation error and analyze the algorithm’s time and memory complexity, demonstrating that the approach provides a feasible solution to the problem

Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

Three-dimensional localization and mapping of static environments by means of mobile perception

Model-based task planning is one of the main capabilities of autonomous mobile robots. Especially for model-based localization and path planning, a large-scale description of the operation environment is required. Cognitive communication between man and his machine could be based on a common, three-dimensional understanding of the environment. In the case of a personal service robot, the operation environment may comprise both indoor and outdoor spaces. In this thesis, a method for the generation of a three-dimensional geometric model for large scale, structured and natural environments is presented. The environment mapping method, which uses range images as measurement data, consists of three main phases: first, geometric features are extracted from each of the range images. Secondly, the relative coordinate transformations (i.e. registrations) between the sensor viewpoint locations, where the range data was measured, are computed. And, finally, an integrated map is formed by transforming the sub-map data into a common frame of reference. Two types of geometric features are extracted from the range images: cylinder segments (or more generally truncated cone segments) and straight-line segments. With cylinder segments tree trunks and other elongated cylindrical objects can be modeled, whereas the straight line segments correspond to the upper corners of vertical walls. The features are utilized as natural landmarks for registration computation. The presented method is tested by mapping three test sites representing structured, semi-structured and natural environments. The structured environment corresponds to a part of the premises of an office building, the semi-structured environment corresponds to the surroundings of a parking lot and the natural environment is a small forest area. The dimensions of the test sites are about 50 meters, 120 meters and 40 meters square, respectively. A simple incremental approach is used to build an integrated model for the parking lot and office corridor environments. For the principal mapping experiment, concerning the small forest area, a statistically more sound, optimal approach is applied. With respect to the feature extraction methods and the computation of the relative coordinate transformations between the viewpoints, robustness to outlier data and failure modes of the methods are discussed in more detail.reviewe

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots