Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking

• URL: http://arxiv.org/abs/2507.04762v2
• Date: Tue, 15 Jul 2025 13:29:17 GMT
• Title: Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking
• Authors: Maria Damanaki, Ioulia Kapsali, Nikos Piperigkos, Alexandros Gkillas, Aris S. Lalos,
• Abstract summary: This paper proposes a novel mitigation framework on 3D LiDAR scene against adversarial noise.<n>We employ the least-squares graph tool to reduce the induced positional error of each detection's centroid.<n>An extensive evaluation study on the real-world V2V4Real dataset demonstrates that the proposed method significantly outperforms both single and multi-agent tracking frameworks.
• Score: 43.11267507022928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The critical perception capabilities of EdgeAI systems, such as autonomous vehicles, are required to be resilient against adversarial threats, by enabling accurate identification and localization of multiple objects in the scene over time, mitigating their impact. Single-agent tracking offers resilience to adversarial attacks but lacks situational awareness, underscoring the need for multi-agent cooperation to enhance context understanding and robustness. This paper proposes a novel mitigation framework on 3D LiDAR scene against adversarial noise by tracking objects based on least-squares graph on multi-agent adversarial bounding boxes. Specifically, we employ the least-squares graph tool to reduce the induced positional error of each detection's centroid utilizing overlapped bounding boxes on a fully connected graph via differential coordinates and anchor points. Hence, the multi-vehicle detections are fused and refined mitigating the adversarial impact, and associated with existing tracks in two stages performing tracking to further suppress the adversarial threat. An extensive evaluation study on the real-world V2V4Real dataset demonstrates that the proposed method significantly outperforms both state-of-the-art single and multi-agent tracking frameworks by up to 23.3% under challenging adversarial conditions, operating as a resilient approach without relying on additional defense mechanisms.

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