# Tiny LiDARs for Manipulator Self-Awareness: Sensor Characterization and Initial Localization Experiments

arXiv: https://arxiv.org/abs/2503.03449 · HTML: https://mlsanity.com/paper/2503.03449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Estimating Scene Flow in Robot Surroundings with Distributed Miniaturized Time-of-Flight Sensors](https://mlsanity.com/paper/2504.02439/) (2025)
- [Task-Driven Implicit Representations for Automated Design of LiDAR Systems](https://mlsanity.com/paper/2505.22344/) (2025)

## Abstract (arXiv)

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.