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  • Robot Perception for Indoor Navigation《室内导航中的机器人感知》

    Felix Endres 论文下载

    Technische Fakult¨ at
    Albert-Ludwigs-Universit¨ at Freiburg

    Betreuer: Prof. Dr. Wolfram Burgard

    简介:In recent years, commercially available mobile robots, that operate in indoor environments, have found their ways into private homes, office environments, and industrial settings.They fulfill important duties such as transportation, telepresence, and cleaning.While some tasks, such as vacuum cleaning, can be achieved with rudimentary perception, for more complex tasks sophisticated perception capabilities are fundamental for autonomous robots.

    近些年来,商业上可用的移动机器人,在室内环境中运动,已经找到了进入私人家庭、办公环境和工厂设施的出路。它们填补了重要的任务比如交通、远程监控和清洁。
     
     Even for the mentioned vacuum cleaning, improved perception al-
    lows for substantial increases in efficiency. In this thesis, we investigate novel approaches
    for the modelling of indoor environments for robot navigation. Being an important foun-
    dation for higher level skills, a particular focus lies on simultaneous localization and
    mapping (SLAM), which allows a robot to construct a model of its environment dur-
    ing operation. In the context of SLAM, we develop an approach for RGB-D cameras,
    that captures dense 3D maps for robot navigation. For this SLAM system, we propose
    novel methods to increase the accuracy of the trajectory estimation and the robustness
    against misassociations during individual motion estimates. Further, we address a major
    limitation on the hardware side of RGB-D cameras, namely the limited field of view. We
    investigate SLAMwith multiple RGB-D cameras and develop an approach for automated
    extrinsic calibration of RGB-D cameras via SLAM. We further propose an extension of
    RGB-D sensors with mirrors to bisect the field of view into two roughly opposite views.
    While this does not increase the overall information perceived, we show that the divided
    field of view is beneficial in the context of SLAM. Additionally, we exploit the structural
    properties of this catadioptric extension to constrain the mentioned calibration method,
    such that planar motion of the robot is sufficient for online calibration of the two views.
    To autonomously access all areas in a private home or office, a further key skill for robot
    navigation is the operation of doors. In this context, we extend the state of the art by novel
    methods for learning a model of the kinematics and dynamics of a door. We demonstrate
    that the knowledge about the dynamics of a door allows the robot to accurately predict
    the motion of the door from inertia. We use this ability to employ a door opening strategy
    with low requirements on the dexterous workspace of the manipulator. To show the bene-
    fits of the approaches proposed in this thesis, we thoroughly evaluate them in experiments
    with real robots and real sensor data. Overall, the proposed approaches lower the cost of
    the sensing equipment and the required complexity of the manipulator. These factors are
    particular important for commercial robots targeted at households and small businesses.

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  • 原文地址:https://www.cnblogs.com/2008nmj/p/6143639.html
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