Contributions to Localization, Mapping and Navigation in Mobile Robotics, PhD Thesis, Jose-Luis Blanco-Claraco, November 13th, 2009.
Downloads: PDF (12.6Mb) – Citation (Bibtex) – Slides (17.5 Mb) – Slides+videos (196 Mb)
Abstract: This thesis focuses on the problem of enabling mobile robots to autonomously build world models of their environments and to employ them as a reference to self–localization and navigation. For mobile robots to become truly autonomous and useful, they must be able of reliably moving towards the locations required by their tasks. This simple requirement gives raise to a myriad of problems that has populated research in the mobile robotics community for decades. Among these issues, two of the most relevant are: (i) secure autonomous navigation while avoiding collisions and (ii) the employment of an adequate world model for robot self-referencing within the environment and also for locating places of interest. In spite of presenting contributions regarding mobile robot navigation, the main focus of this thesis is on the latter problem, usually referred to as Simultaneous Localization and Mapping (SLAM). One of the most interesting contributions of this thesis is a novel approach to extend SLAM to large-scale scenarios by means of a seamless integration of geometric and topological map building in a probabilistic framework that estimates the hybrid metric-topological (HMT) state space of the robot path. The proposed framework unifies in an elegant manner the research areas of topological mapping, reasoning on topological maps and metric SLAM, providing also a natural integration of SLAM and the “robot awakening” problem. Other contributions presented in this thesis cover a wide variety of topics, such as optimal estimation in particle filters, a new probabilistic observation model for laser scanners based on consensus theory, a novel measure of the uncertainty in grid mapping, an efficient method for range-only SLAM, a grounded method for partitioning large maps into submaps, a multi-hypotheses approach to grid map matching, and a mathematical framework for extending simple obstacle avoidance methods to realistic robots.
- 2011: “Premio extraordinario de Doctorado“, given by the “Escuela Superior de Ingeniería Industrial” of the University of Málaga.
- 2010: ABB national prize for the best PhD thesis in Robotics for the year 2009, by the National group of Robotics (GTRob-CEA).
Implementations in MRPT
Virtually all the developments and experiments presented in this thesis have been firstly integrated and tested in the MRPT project. Next follows a list of the contributions and their corresponding parts in MRPT:
- CHAPTER 4: Optimal Bayesian filtering for non-parametric observation models: Already explained here.
- CHAPTER 5: A consensus-based observation likelihood model for precise sensors: This method refers to the choice lmConsensus and the switch LF_alternateAverageMethod in the virtual methods to evaluate observation likelihoods in MRPT metric maps.
- CHAPTER 8: Optimal particle filtering in SLAM: The optimal PF method is named pfAuxiliaryPFOptimal in MRPT. See this page for more info.
- CHAPTER 9: An efficient solution to range-only SLAM: This method already has its page.
- CHAPTER 10: Measuring uncertainty in SLAM and exploration: The EMI and EMMI correspond to different measures of the uncertainty implemented in the class for occupancy grid maps and in RBPF-SLAM for grid maps.
- CHAPTER 12: Hybrid Metric Topological SLAM: See the HMT-SLAM page (JAN-2010: at the present, still in construction).
- CHAPTER 13: Clustering observations into local maps: See its page for code and applications.
- CHAPTER 14: Grid map matching for topological loop closure: See the method page.
- CHAPTER 16: Reactive navigation based on PTGs: See the page on the PTG reactive navigator.