With the increasing prevalence of robots in daily life, it is crucial to enable robots to construct a reliable map online to navigate in unbounded and changing environments. Although existing methods can individually achieve the goals of spatial mapping and dynamic object detection and tracking, limited research has been conducted on an effective combination of these two important abilities. The proposed framework, SMAT (Simultaneous Mapping and Tracking), integrates the front-end dynamic object detection and tracking module with the back-end static mapping module using a self-reinforcing mechanism, which promotes mutual improvement of mapping and tracking performance. The conducted experiments demonstrate the framework's effectiveness in real-world applications, achieving successful long-range navigation and mapping in multiple urban environments using only one LiDAR, a CPU-only onboard computer, and a consumer-level GPS receiver.

翻译：随着机器人在日常生活中的日益普及，使机器人能够在无界且动态变化的环境中在线构建可靠地图至关重要。尽管现有方法可分别实现空间建图与动态目标检测追踪的目标，但针对这两种重要能力的有效结合研究仍十分有限。本文提出的SMAT（同步建图与追踪）框架，通过自强化机制将前端动态目标检测追踪模块与后端静态建图模块相集成，从而促进建图与追踪性能的相互提升。实验表明，该框架在实际应用中具有有效性——仅使用单个激光雷达、纯CPU车载计算机及消费级GPS接收器，即可在多个城市环境中实现成功的长距离导航与建图。