GSLAMOT：一种基于轨迹子图与查询图的同步定位、建图与多目标跟踪系统
GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System
AI总结：
针对陌生环境下同步定位建图与多目标3D位姿跟踪的需求，提出基于轨迹子图和查询图的GSLAMOT框架，结合多模态输入、多准则关联与图优化，在多数据集上实现优于现有方法的性能。
摘要：
为在陌生环境中与移动对象交互，同步定位、建图并跟踪多个对象的3D位姿是核心需求。本文提出一种基于轨迹子图（Tracklet Graph）与查询图（Query Graph）的框架GSLAMOT，以解决这一挑战。GSLAMOT以相机和LiDAR的多模态信息为输入，将动态场景的表征划分为三部分：用于表示静态环境的语义地图、自智能体的轨迹，以及用于跟踪和预测检测到的移动对象3D位姿的在线维护轨迹子图（TG）。每帧通过目标检测构建查询图（QG），用于查询和更新TG。为实现精准的目标关联，本文提出多准则星图关联（MSGA）方法，用于在QG中的检测结果与TG中的预测轨迹子图之间寻找匹配对象。随后，本文提出以目标为中心的图优化（OGO）方法，可同步优化TG、语义地图和智能体轨迹；该方法将检测到的目标三角化纳入地图，以丰富地图的语义信息。本文还解决了效率问题，可并行处理这三项紧密耦合的任务。实验在KITTI、Waymo以及一个突出挑战性场景的模拟交通拥堵数据集上开展。实验结果表明，GSLAMOT在挑战性场景下精准执行SLAM的同时，可实现精准的拥挤目标跟踪，性能优于现有最优方法。代码和数据集可在https://gslamot.github.io获取。
英文摘要：
For interacting with mobile objects in unfamiliar environments, simultaneously locating, mapping, and tracking the 3D poses of multiple objects are crucially required. This paper proposes a Tracklet Graph and Query Graph-based framework, i.e., GSLAMOT, to address this challenge. GSLAMOT utilizes camera and LiDAR multimodal information as inputs and divides the representation of the dynamic scene into a semantic map for representing the static environment, a trajectory of the ego-agent, and an online maintained Tracklet Graph (TG) for tracking and predicting the 3D poses of the detected mobile objects. A Query Graph (QG) is constructed in each frame by object detection to query and update TG. For accurate object association, a Multi-criteria Star Graph Association (MSGA) method is proposed to find matched objects between the detections in QG and the predicted tracklets in TG. Then, an Object-centric Graph Optimization (OGO) method is proposed to simultaneously optimize the TG, the semantic map, and the agent trajectory. It triangulates the detected objects into the map to enrich the map's semantic information. We address the efficiency issues to handle the three tightly coupled tasks in parallel. Experiments are conducted on KITTI, Waymo, and an emulated Traffic Congestion dataset that highlights challenging scenarios. Experiments show that GSLAMOT enables accurate crowded object tracking while conducting SLAM accurately in challenging scenarios, demonstrating more excellent performances than the state-of-the-art methods. The code and dataset are at https://gslamot.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
