Tiny LiDARs for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916873102163968 |
|---|---|
| author | Caroleo, Giammarco Albini, Alessandro De Martini, Daniele Barfoot, Timothy D. Maiolino, Perla |
| author_facet | Caroleo, Giammarco Albini, Alessandro De Martini, Daniele Barfoot, Timothy D. Maiolino, Perla |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03449 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tiny LiDARs for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments Caroleo, Giammarco Albini, Alessandro De Martini, Daniele Barfoot, Timothy D. Maiolino, Perla Robotics 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. |
| title | Tiny LiDARs for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.03449 |