2025/03/05 by Giammarco Caroleo, Caroleo, Giammarco, Alessandro Albini +7 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Advanced Optical Sensing Technologies #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.2503.03449
openalex publication_date 2025/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.