改进卡尔曼滤波，让自动驾驶更准地追踪遮挡物体
Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking
- 用高斯分布建模运动变化，自适应调整运动模型
- 在多个数据集上提升轨迹预测精度，延迟低于多模型方法
- 特别适合遮挡和静止目标的稳定跟踪
精确的3D多目标跟踪状态估计对自动驾驶至关重要，尤其在目标遮挡场景下。现有方法普遍采用恒定运动假设的卡尔曼滤波，但忽视了城市交通中物体运动的连续变化。尽管已有研究引入多模型卡尔曼滤波，但其同时处理多个模型带来显著计算开销。为此，本文提出运动动力学卡尔曼滤波（MD-KF），在不破坏运动模型单一性前提下，克服恒定运动假设的局限。MD-KF将连续观测间物体运动的变化建模为高斯分布，并自适应调整加权运动模型以应对这些变化。实验表明，MD-KF在多个数据集上均优于恒定运动和多模型卡尔曼滤波，且计算延迟显著低于多模型方法。该方法在遮挡期间轨迹估计和静止物体状态稳定性方面表现更优。
原文摘要 · Abstract (English)
Precise 3D state estimation in multi-object tracking (MOT) is critical for self-driving cars, particularly for objects occluded. Motion modeling in the Kalman filter with a constant motion assumption is widely used in MOT methods, but it neglects the continuous changes in objects' motion caused by traffic in urban environments. Although recent research introduces a multimodel Kalman filter that incorporates multiple motion models, these approaches incur significant computational overhead from the simultaneous processing of multiple models. To this end, this work introduces a motion-dynamics Kalman filter (MD-KF) that overcomes the constant-motion assumption while preserving the singularity of the motion model. MD-KF models the changes in objects' motion over successive measurements as Gaussian distributions, and adaptively adjusts a weighted motion model to account for these variations. MD-KF consistently outperforms constant and multimodel KF across multiple datasets with a significant reduction in computation latency compared to multimodel approaches. The proposed approach demonstrates its superiority in trajectory estimation during occlusion and state estimation stability for stationary objects.
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