Robust Collaborative 3D Object Detection in Presence of Pose Errors (2211.07214v3)

Abstract: Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect localization would cause spatial message misalignment and significantly reduce the performance of collaboration. To alleviate adverse impacts of pose errors, we propose CoAlign, a novel hybrid collaboration framework that is robust to unknown pose errors. The proposed solution relies on a novel agent-object pose graph modeling to enhance pose consistency among collaborating agents. Furthermore, we adopt a multi-scale data fusion strategy to aggregate intermediate features at multiple spatial resolutions. Comparing with previous works, which require ground-truth pose for training supervision, our proposed CoAlign is more practical since it doesn't require any ground-truth pose supervision in the training and makes no specific assumptions on pose errors. Extensive evaluation of the proposed method is carried out on multiple datasets, certifying that CoAlign significantly reduce relative localization error and achieving the state of art detection performance when pose errors exist. Code are made available for the use of the research community at https://github.com/yifanlu0227/CoAlign.

• The paper introduces CoAlign, a framework that merges intermediate and late collaboration paradigms to address pose misalignments.
• It utilizes an agent-object pose graph that reduces relative pose errors by up to 75% and enhances detection accuracy by at least 12%.
• The approach advances multi-agent perception in autonomous vehicles and robotics by ensuring robust 3D detection despite localization challenges.

Robust Collaborative 3D Object Detection in Presence of Pose Errors

The research paper titled "Robust Collaborative 3D Object Detection in Presence of Pose Errors" introduces a novel framework, termed $\mathtt{CoAlign}$, focused on enhancing the robustness of collaborative 3D object detection systems confronted with pose estimation inaccuracies. The essence of collaborative 3D object detection lies in leveraging multiple sensor inputs across agents to mitigate sensor limitations such as occlusion. Nevertheless, pose errors, which stem from imperfect localization, remain a significant hurdle, potentially misaligning spatial messages and degrading the effectiveness of collaboration. This paper addresses this challenge by introducing methodologies that are adept at detecting objects in such uncertain environments without the dependence on precise ground-truth pose data.

The authors propose $\mathtt{CoAlign$, a hybrid framework that amalgamates intermediate and late collaboration paradigms. A pivotal component of this framework is an agent-object pose graph optimization mechanism that ensures pose alignment without necessitating accurate pose supervision during the training phase. Instead, this framework focuses on achieving pose consistency by modeling the spatial relations between agents and objects detected within the scene. This approach leverages an agent-object pose graph, a bipartite construction that enables the alignment of detected objects across multiple spatial viewpoints, promoting consistency among pose estimations from different agent perspectives. This framework stands out by not requiring any specific assumptions surrounding pose errors and is therefore broadly applicable.

The paper conducts an extensive evaluation of the proposed method across numerous datasets, including OPV2V, V2X-Sim 2.0, and DAIR-V2X, demonstrating that $\mathtt{CoAlign}$ offers superior performance in terms of reducing relative localization errors and achieving state-of-the-art detection outcomes in scenarios burdened with pose estimation errors. The results substantiate the practical advantage of $\mathtt{CoAlign}$, highlighting its ability to correct up to 75% of relative pose errors and achieve at least 12% improvement in the accuracy of collaborative 3D detection tasks in the presence of such errors.

The significance of the research extends beyond immediate practical benefits; theoretically, it poses implications for the development and deployment of multi-agent perception systems in autonomous vehicles, robotics, and related fields. The ability to perform robust 3D detection despite localization challenges mitigates a major barrier in deploying collaborative perception in real-world scenarios, where noise and errors are inescapable realities.

Potential future directions proffered by this paper include expanding $\mathtt{CoAlign}$ to multimodal data settings. Integrating different sensory inputs could lead to even greater robustness and adaptability in complex environments. Moreover, further research might focus on enhancing the computational efficiency of the framework to accommodate real-time processing requirements inherent in dynamic operational scenarios.

In summary, the paper presents an insightful approach to collaborative 3D object detection, focusing on overcoming the intricacies posed by pose estimation errors through innovative graph modeling and data fusion strategies, thereby charting a course for more resilient multi-agent systems in complex environments.