用分布式低分辨率激光传感器实现柔性机器人的高精度定位
Continuum Robot Localization using Distributed Time-of-Flight Sensors
- 在机器人本体上分布多个小型激光传感器，结合形状先验进行融合定位
- 53厘米长的机器人定位误差平均为2.5厘米（位置）和7.2度（姿态）
- 适用于复杂环境，对先验地图偏差具有鲁棒性，适合柔性机器人应用
在非结构化环境中，定位与建图是机器人作业的关键任务。虽然时间飞行（ToF）传感器（如激光雷达）在移动机器人中已被证明有效，但其高分辨率特性使其难以应用于软体和连续体机器人（CR）。此外，这类机器人的可变形特性也导致其在非结构化环境中的定位与建图长期未被充分研究。本文提出一种基于沿机器人长度分布的小型、低分辨率ToF传感器的定位方法。通过融合测量信息与机器人形状先验，即使各传感器频繁遭遇退化情形，仍能实现高精度定位。在所有实验条件下，53厘米长的机器人平均定位误差为2.5厘米（位置）和7.2°（旋转）。结果在多种环境、仿真与真实实验中均具重复性，并验证了估计对先验地图偏差的鲁棒性。
原文摘要 · Abstract (English)
Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g.,~lidar) have proven useful in mobile robotics, where high-resolution sensors can be used for simultaneous localization and mapping. In soft and continuum robotics, however, these high-resolution sensors are too large for practical use. This, combined with the deformable nature of such robots, has resulted in continuum robot (CR) localization and mapping in unstructured environments being a largely untouched area. In this work, we present a localization technique for CRs that relies on small, low-resolution ToF sensors distributed along the length of the robot. By fusing measurement information with a robot shape prior, we show that accurate localization is possible despite each sensor experiencing frequent degenerate scenarios. We achieve an average localization error of 2.5cm in position and 7.2° in rotation across all experimental conditions with a 53cm long robot. We demonstrate that the results are repeated across multiple environments, in both simulation and real-world experiments, and study robustness in the estimation to deviations in the prior map.
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