Occupancy grids are the most common framework when it comes to creating a map of the environment using a robot. This paper studies occupancy grids from the motion planning perspective and proposes a mapping method that provides richer data (map) for the purpose of planning and collision avoidance. Typically, in occupancy grid mapping, each cell contains a single number representing the probability of cell being occupied. This leads to conflicts in the map, and more importantly inconsistency between the map error and reported confidence values. Such inconsistencies pose challenges for the planner that relies on the generated map for planning motions. In this work, we store a richer data at each voxel including an accurate estimate of the variance of occupancy. We show that in addition to achieving maps that are often more accurate than tradition methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and its reported confidence. This allows the planner to reason about acquisition of the future sensory information. Such planning can lead to active perception maneuvers that while guiding the robot toward the goal aims at increasing the confidence in parts of the map that are relevant to accomplishing the task.
Grid-based Submap Joining: An Efficient Algorithm for Simultaneously Optimizing Global Occupancy Map and Local Submap Frames
Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
State Key Laboratory of Ocean Sensing, Zhejiang University（浙江大学海洋传感国家重点实验室）；Interdisciplinary Student Training Platform for Marine areas, Zhejiang University（浙江大学海洋交叉学科学生培养平台）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；School of AI and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Ocean College, Zhejiang University（浙江大学海洋学院）；Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）
