改进点云分割追踪，减少身份错乱，尤其提升小物体追踪效果
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation Data
- 引入卡尔曼滤波估运动，结合轨迹状态管理提升跟踪优先级
- 在SemanticKITTI上小物体追踪误标识减少，启动更早且更稳定
- 适合自动驾驶与机器人场景中复杂环境下的长时序感知
4D全景激光雷达分割对自动驾驶和机器人场景理解至关重要，融合语义与实例分割并保持时间一致性。现有方法如4D-PLS和4D-STOP采用检测跟踪范式，在每帧使用深度网络进行分割，并通过当前帧与前几帧的大型实例匹配来维持时序一致性。然而，其依赖短期实例检测、缺乏运动估计，且忽略小尺寸实例，导致频繁身份切换，跟踪性能下降。本文提出NextStop追踪器，集成基于卡尔曼滤波的运动估计、数据关联与生命周期管理，并引入轨迹段状态概念以优化优先级。在SemanticKITTI验证集上，以LSTQ指标评估，NextStop显著提升小物体（如行人、自行车）的追踪表现，减少身份错乱，提前启动追踪，复杂环境下可靠性更高。代码已开源。
原文摘要 · Abstract (English)
4D panoptic LiDAR segmentation is essential for scene understanding in autonomous driving and robotics, combining semantic and instance segmentation with temporal consistency. Current methods, like 4D-PLS and 4D-STOP, use a tracking-by-detection methodology, employing deep learning networks to perform semantic and instance segmentation on each frame. To maintain temporal consistency, large-size instances detected in the current frame are compared and associated with instances within a temporal window that includes the current and preceding frames. However, their reliance on short-term instance detection, lack of motion estimation, and exclusion of small-sized instances lead to frequent identity switches and reduced tracking performance. We address these issues with the NextStop1 tracker, which integrates Kalman filter-based motion estimation, data association, and lifespan management, along with a tracklet state concept to improve prioritization. Evaluated using the LiDAR Segmentation and Tracking Quality (LSTQ) metric on the SemanticKITTI validation set, NextStop demonstrated enhanced tracking performance, particularly for small-sized objects like people and bicyclists, with fewer ID switches, earlier tracking initiation, and improved reliability in complex environments. The source code is available at https://github.com/AIROTAU/NextStop
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