Tiny LiDARs for manipulator self-awareness: sensor characterization and initial localization experiments

Preprint

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.

Authors: Caroleo, G; Albini, A; De Martini, D; Barfoot, TD; Maiolino, P

• Publication status: Published
• Peer review status: Reviewed (other)
• Preprint server: arXiv
• Publication date: 2025-03-05
• DOI: 10.48550/arXiv.2503.03449
• Server owner: Cornell University
• Language: English