论文标题
触觉顺序的蒙特卡洛定位，用于在视力贬值的情况下进行四倍的运动
Haptic Sequential Monte Carlo Localization for Quadrupedal Locomotion in Vision-Denied Scenarios
论文作者
论文摘要
在地下矿山或下水道等极端情况下，连续的机器人操作很困难，因为外观感受性传感器可能由于雾，黑暗，污垢或故障而失败。为了在这种情况下启用自主导航，我们开发了一种本体感受的本地化，该定位利用了四倍的机器人进行的脚接触，以在没有任何相机或激光镜头传感器的帮助下，以在先前的环境图上进行定位。提出的方法使机器人在在地形特征上制定了一系列接触事件后，可以准确地重新定位自身。该方法基于顺序蒙特卡洛，可以支持2.5D和3D先前的地图表示。我们已经在网上测试了该方法，并在两种不同的情况下在船上进行了Anymal四倍的机器人：定制建造的木制地形课程的遍历以及墙壁探测和遵循任务。在这两种情况下，机器人都能够有效地实现本地化匹配并执行所需的预计划路径。该方法仅使用其脚，运动学和惯性传感，在丰富的地形上将定位误差降至10厘米。
Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of situations, we have developed a type of proprioceptive localization which exploits the foot contacts made by a quadruped robot to localize against a prior map of an environment, without the help of any camera or LIDAR sensor. The proposed method enables the robot to accurately re-localize itself after making a sequence of contact events over a terrain feature. The method is based on Sequential Monte Carlo and can support both 2.5D and 3D prior map representations. We have tested the approach online and onboard the ANYmal quadruped robot in two different scenarios: the traversal of a custom built wooden terrain course and a wall probing and following task. In both scenarios, the robot is able to effectively achieve a localization match and to execute a desired pre-planned path. The method keeps the localization error down to 10cm on feature rich terrain by only using its feet, kinematic and inertial sensing.
