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.

翻译：在自动驾驶或机器人领域，同步定位与建图（SLAM）和多目标跟踪（MOT）是两个基本问题，通常被分开应用。SLAM和MOT的解决方案通常依赖于某些假设，例如SLAM的静态环境假设和MOT的精确自车姿态假设。但在复杂的动态环境中，这些假设难以甚至无法满足。因此，针对动态环境中的自动驾驶车辆，出现了SLAMMOT，即同步定位、建图与移动目标跟踪——一种SLAM与目标跟踪的集成系统。然而，许多传统的SLAMMOT解决方案直接对目标跟踪的预测和检测进行数据关联，却忽略了它们的质量。实际上，在临时遮挡场景中，由于连续多帧漏检导致的不准确预测，可能会降低跟踪性能，从而影响SLAMMOT。为应对这一挑战，本文提出了一种基于置信度引导数据关联的激光雷达SLAMMOT（Conf SLAMMOT）方法，该方法将激光雷达SLAM与基于置信度引导数据关联的多目标跟踪紧密耦合到一个图优化后端中，以同时估计自车与目标的状态。预测和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器的方法中因初始分配错误导致的性能下降，还能处理跟踪中的连续漏检等问题，同时提升了SLAMMOT的整体性能。多种对比实验证明了Conf SLAMMOT的显著优势，尤其是在存在部分漏检的场景中。