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 that is validated 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.
View on arXiv@article{caroleo2025_2503.03449,
title={ Tiny Lidars for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments },
author={ Giammarco Caroleo and Alessandro Albini and Daniele De Martini and Timothy D. Barfoot and Perla Maiolino },
journal={arXiv preprint arXiv:2503.03449},
year={ 2025 }
}