D2SR: Decentralized Detection, De-Synchronization, and Recovery of LiDAR Interference

We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination am...

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Bibliographic Details
Main Authors: Rathnayake, Darshana, Sabbella, Hemanth, Radhakrishnan, Meera, Misra, Archan
Format: Conference Proceeding
Language:English
Subjects:
Depth measurement
Interference
Laser radar
LiDAR Interference
Multi-Robot Systems
Performance evaluation
Pipelines
Prevention and mitigation
Robot kinematics
Robustness
Sensors
Throughput
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Summary:We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination among nearby LiDAR devices. D2SR comprises three stages: (a) Detection, which identifies interfered frames, (b) Mitigation, which performs time-shifting of a LiDAR's active period to reduce interference, and (c) Recovery, which corrects or reconstructs the depth values in interfered regions of a depth frame. Key contributions include a lightweight interference detection algorithm achieving an F1-score of 92%, a simple yet effective decentralized de-synchronization mechanism, and a lightweight depth recovery pipeline that preserves high throughput processing on edge devices. Evaluation on Nvidia Jetson devices demonstrates D2SR's efficacy: under static settings, D2SR accurately detects interference in 93% of cases (recall=82%) and reduces the depth estimation error by 27% (RMSE= 38.7 cm, compared to RMSE= 60.6 cm for a baseline without D2SR). Furthermore, D2SR is able to reduce the fraction of interfered frames by 75.1% and reduce the depth estimation error (for interfered frames) by 24.9% even for a moving robot scenario.
ISSN:2153-0866
DOI:10.1109/IROS58592.2024.10801437