D2SR: Decentralized detection, de-synchronization, and recovery of LiDAR interference

• Main Authors: RATHNAYAKE, Darshana, SABBELLA, Hemanth, RADHAKRISHNAN, Meera, MISRA, Archan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: LiDAR Interference; Multi-Robot Systems; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9230; https://ink.library.smu.edu.sg/context/sis_research/article/10227/viewcontent/IROS2024_D2SR_Camera_Ready.pdf
• Institution: Singapore Management University

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.