面向崎岖地形移动地面机器人的有符号距离场精确位姿预测
Accurate Pose Prediction on Signed Distance Fields for Mobile Ground Robots in Rough Terrain
AI总结：
本文提出一种基于有符号距离场的迭代几何方法，用于精确预测崎岖地形中带主动鳍板移动机器人的三维位姿，在仿真和真实平台上验证了其高精度与在线规划能力，并优于现有高度图方法。
摘要：
移动地面机器人在非结构化环境中的自主运动（例如航点导航或鳍板控制）需要对机器人与地形之间的交互进行足够精确的预测。占用栅格或可通行性地图等启发式方法被广泛使用，但由于未考虑关节位置，它们限制了配备主动鳍板的机器人可执行的动作。我们提出了一种新颖的迭代几何方法，用于在不平坦地面上高精度地预测配备主动鳍板的移动地面机器人的三维位姿，并具备在线规划能力。该方法利用有符号距离场以亚体素精度表示表面的能力来实现。所提方法的有效性在仿真中的两种不同履带式机器人以及一个真实平台上得到了验证。与作为真值的跟踪系统相比，我们的方法预测机器人位置和姿态的平均精度分别为3.11厘米和3.91°，优于最近一种基于高度图的方法。该实现已作为开源ROS软件包提供。
英文摘要：
Autonomous locomotion for mobile ground robots in unstructured environments such as waypoint navigation or flipper control requires a sufficiently accurate prediction of the robot-terrain interaction. Heuristics like occupancy grids or traversability maps are widely used but limit actions available to robots with active flippers as joint positions are not taken into account. We present a novel iterative geometric method to predict the 3D pose of mobile ground robots with active flippers on uneven ground with high accuracy and online planning capabilities. This is achieved by utilizing the ability of signed distance fields to represent surfaces with sub-voxel accuracy. The effectiveness of the presented approach is demonstrated on two different tracked robots in simulation and on a real platform. Compared to a tracking system as ground truth, our method predicts the robot position and orientation with an average accuracy of 3.11 cm and 3.91°, outperforming a recent heightmap-based approach. The implementation is made available as an open-source ROS package.
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