融合激光雷达与多相机，突破视角重叠限制，提升自动驾驶3D多目标追踪性能。
3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving
- 引入激光雷达测量模型，构建多传感器融合的3D多目标追踪框架
- 在nuScenes数据集上追踪精度显著优于现有MS-GLMB方法
- 无需摄像头视野重叠，适用于更复杂交通场景，适合自动驾驶研发
MS-GLMB滤波器通过多传感器数据实现了多目标跟踪的鲁棒性。在此基础上，MV-GLMB与MV-GLMB-AB滤波器利用摄像头实现3D多传感器多目标跟踪，有效缓解遮挡问题。但二者依赖摄像头视野重叠以融合互补信息。本文提出一种改进方法，将激光雷达（LiDAR）集成至MS-GLMB框架中，设计新的激光雷达测量模型及多相机-激光雷达联合测量模型。实验结果表明，该方法在nuScenes数据集上的跟踪性能显著优于现有基于MS-GLMB的方法。更重要的是，本方法摆脱了对摄像头视野重叠的依赖，扩展了MS-GLMB滤波器的应用范围。代码已开源：https://github.com/linh-gist/ms-glmb-nuScenes。
原文摘要 · Abstract (English)
The MS-GLMB filter offers a robust framework for tracking multiple objects through the use of multi-sensor data. Building on this, the MV-GLMB and MV-GLMB-AB filters enhance the MS-GLMB capabilities by employing cameras for 3D multi-sensor multi-object tracking, effectively addressing occlusions. However, both filters depend on overlapping fields of view from the cameras to combine complementary information. In this paper, we introduce an improved approach that integrates an additional sensor, such as LiDAR, into the MS-GLMB framework for 3D multi-object tracking. Specifically, we present a new LiDAR measurement model, along with a multi-camera and LiDAR multi-object measurement model. Our experimental results demonstrate a significant improvement in tracking performance compared to existing MS-GLMB-based methods. Importantly, our method eliminates the need for overlapping fields of view, broadening the applicability of the MS-GLMB filter. Our source code for nuScenes dataset is available at https://github.com/linh-gist/ms-glmb-nuScenes.
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