用B样条曲线建模物体侧视轮廓，实现更精准的3D目标追踪。
3D Extended Object Tracking based on Extruded B-Spline Side View Profiles
- 用B样条曲线描述物体侧视轮廓，通过拉伸生成3D形状。
- 在真实雷达与激光雷达数据集上，追踪精度优于传统方法。
- 适合需要高精度3D目标感知的自动驾驶系统使用。
目标追踪是自动驾驶系统的核心任务。随着3D传感器的发展，采用高效的3D扩展目标追踪（EOT）方法可显著提升环境感知能力。基于常见道路参与者在行驶方向上左右对称的观察，本文聚焦于物体的侧视轮廓。为利用2D EOT的研究成果并控制形状模型参数量，提出一种基于B样条曲线描述物体侧视轮廓，并通过拉伸生成3D延伸范围的3D EOT方法。B样条曲线具有灵活的表达能力，允许控制点自由移动。该算法被集成至扩展卡尔曼滤波器（EKF）框架中。通过模拟不同车型的交通场景及包含雷达与激光雷达的真实开放数据集进行了全面评估。
原文摘要 · Abstract (English)
Object tracking is an essential task for autonomous systems. With the advancement of 3D sensors, these systems can better perceive their surroundings using effective 3D Extended Object Tracking (EOT) methods. Based on the observation that common road users are symmetrical on the right and left sides in the traveling direction, we focus on the side view profile of the object. In order to leverage of the development in 2D EOT and balance the number of parameters of a shape model in the tracking algorithms, we propose a method for 3D extended object tracking (EOT) by describing the side view profile of the object with B-spline curves and forming an extrusion to obtain a 3D extent. The use of B-spline curves exploits their flexible representation power by allowing the control points to move freely. The algorithm is developed into an Extended Kalman Filter (EKF). For a through evaluation of this method, we use simulated traffic scenario of different vehicle models and realworld open dataset containing both radar and lidar data.
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