基于置信度引导数据关联的LiDAR SLAMMOT
LiDAR SLAMMOT based on Confidence-guided Data Association
AI总结：
针对动态场景下漏检导致SLAMMOT跟踪关联失效的问题，提出Conf SLAMMOT，通过置信度引导的因子图数据关联紧耦合LiDAR SLAM与多目标跟踪，显著提升漏检场景性能。
摘要：
在自动驾驶或机器人领域，同步定位与地图构建（SLAM）和多目标跟踪（MOT）是两个基础问题，通常被分别应用。SLAM和MOT的解决方案通常依赖某些假设，例如SLAM的静态环境假设以及MOT的自车精确位姿假设。但在复杂动态环境中，这些假设难以满足，甚至不可能满足。因此，面向动态环境中的自动驾驶车辆，出现了SLAM与目标跟踪的集成系统SLAMMOT，即同步定位、地图构建与运动目标跟踪。然而，许多传统SLAMMOT解决方案直接对目标跟踪的预测结果和检测结果进行数据关联，却忽视了它们的质量。在实际情况中，临时遮挡场景下连续多帧漏检导致的不准确预测，可能降低跟踪性能，进而影响SLAMMOT。为应对这一挑战，本文提出一种基于置信度引导数据关联的LiDAR SLAMMOT（Conf SLAMMOT）方法，该方法将LiDAR SLAM与基于置信度引导数据关联的多目标跟踪紧耦合到图优化后端中，用于同时估计自车和目标的状态。预测与检测的置信度被应用于基于因子图的多目标跟踪的数据关联中，这不仅避免了某些基于滤波的方法中错误初始分配导致的性能下降，还处理了跟踪过程中的连续漏检等问题，同时提升了SLAMMOT的整体性能。多项对比实验证明了Conf SLAMMOT的显著优势，尤其是在存在一定漏检的场景中。
英文摘要：
In the field of autonomous driving or robotics, simultaneous localization and mapping (SLAM) and multi-object tracking (MOT) are two fundamental problems and are generally applied separately. Solutions to SLAM and MOT usually rely on certain assumptions, such as the static environment assumption for SLAM and the accurate ego-vehicle pose assumption for MOT. But in complex dynamic environments, it is difficult or even impossible to meet these assumptions. Therefore, the SLAMMOT, i.e., simultaneous localization, mapping, and moving object tracking, integrated system of SLAM and object tracking, has emerged for autonomous vehicles in dynamic environments. However, many conventional SLAMMOT solutions directly perform data association on the predictions and detections for object tracking, but ignore their quality. In practice, inaccurate predictions caused by continuous multi-frame missed detections in temporary occlusion scenarios, may degrade the performance of tracking, thereby affecting SLAMMOT. To address this challenge, this paper presents a LiDAR SLAMMOT based on confidence-guided data association (Conf SLAMMOT) method, which tightly couples the LiDAR SLAM and the confidence-guided data association based multi-object tracking into a graph optimization backend for estimating the state of the ego-vehicle and objects simultaneously. The confidence of prediction and detection are applied in the factor graph-based multi-object tracking for its data association, which not only avoids the performance degradation caused by incorrect initial assignments in some filter-based methods but also handles issues such as continuous missed detection in tracking while also improving the overall performance of SLAMMOT. Various comparative experiments demonstrate the superior advantages of Conf SLAMMOT, especially in scenes with some missed detections.
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