用微型激光雷达实现机器人自感知，提升目标定位精度
Tiny LiDARs for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments
- 利用微型ToF传感器获取粗略点云数据
- 提出概率模型，定位精度优于两个基准方法
- 适合做机器人末端自感知与小型化传感系统研究
针对操作与检测等任务，让机器人在工作空间中定位目标物体具有重要意义。本文提出一种方法，利用微型VL53L5CX飞行时间（ToF）传感器（即微型激光雷达）获取的粗略点云，实现对目标物体的定位。首先通过实验标定传感器读数与目标相对距离及朝向的关系；随后构建概率传感器模型，并在基于粒子滤波器（PF）的目标位姿估计任务中进行验证。结果表明，所提模型在定位性能上优于两个基线：一是假设测量无误差的模型，二是依赖传感器数据手册提供的置信度模型。
原文摘要 · Abstract (English)
For several tasks, ranging from manipulation to inspection, it is beneficial for robots to localize a target object in their surroundings. In this paper, we propose an approach that utilizes coarse point clouds obtained from miniaturized VL53L5CX Time-of-Flight (ToF) sensors (tiny LiDARs) to localize a target object in the robot's workspace. We first conduct an experimental campaign to calibrate the dependency of sensor readings on relative range and orientation to targets. We then propose a probabilistic sensor model, which we validate in an object pose estimation task using a Particle Filter (PF). The results show that the proposed sensor model improves the performance of the localization of the target object with respect to two baselines: one that assumes measurements are free from uncertainty and one in which the confidence is provided by the sensor datasheet.
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