仅用机器人自身感知信息，实时探测被遮挡的障碍物并估计其位置和尺寸。
PROBE: Proprioceptive Obstacle Detection and Estimation while Navigating in Clutter
- 用关节扭矩与整体运动历史作为输入，通过Transformer网络推断障碍物
- 在仿真与真实机器狗上均实现对遮挡矩形障碍物的精准定位与姿态预测
- 适用于视觉受阻场景，如搜救、复杂环境导航，无需摄像头
在搜救等关键任务中，环境恶化常导致障碍物遮挡，使机载摄像头等视觉传感器因遮挡和视野受限而失效。为应对这一挑战，我们提出一种新方法——基于本体感知的杂乱环境中障碍物检测与估计（PROBE），该方法仅依赖机器人自身的本体感知信息，即可在行进过程中推断出被遮挡的矩形障碍物是否存在，并预测其在SE(2)空间中的尺寸与位姿。所提方法采用Transformer神经网络，输入为历史施加的力矩及感知到的整体运动，输出为环境障碍物的参数化表示。PROBE在Isaac Gym仿真环境及真实单位兔Go1四足机器人上进行了验证，效果显著。
原文摘要 · Abstract (English)
In critical applications, including search-and-rescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, particularly vision, due to occlusion and the constrained range of view of onboard camera sensors. To enable robots to tackle these challenges, we propose a new approach, Proprioceptive Obstacle Detection and Estimation while navigating in clutter PROBE, which instead relies only on the robot's proprioception to infer the presence or absence of occluded rectangular obstacles while predicting their dimensions and poses in SE(2). The proposed approach is a Transformer neural network that receives as input a history of applied torques and sensed whole-body movements of the robot and returns a parameterized representation of the obstacles in the environment. The effectiveness of PROBE is evaluated on simulated environments in Isaac Gym and with a real Unitree Go1 quadruped robot.
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