Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment. This intricate task is further compounded by the inherent "chicken-and-egg" dilemma, where accurate mapping relies on a dependable estimation of the robot's location, and vice versa. Moreover, the computational intensity of SLAM adds an additional layer of complexity, making it a crucial yet demanding topic in the field. In our research, we address the challenges of SLAM by adopting the Particle Filter SLAM method. Our approach leverages encoded data and fiber optic gyro (FOG) information to enable precise estimation of vehicle motion, while lidar technology contributes to environmental perception by providing detailed insights into surrounding obstacles. The integration of these data streams culminates in the establishment of a Particle Filter SLAM framework, representing a key endeavor in this paper to effectively navigate and overcome the complexities associated with simultaneous localization and mapping in robotic systems.

翻译：同时定位与地图构建（SLAM）是机器人领域面临的重大挑战，其涉及在未知环境中动态构建地图的同时准确确定机器人的精确位置。该复杂任务因固有的"鸡生蛋"困境而进一步加剧——精确定位需要可靠的地图，而精确建图又依赖于精准的位置估计。此外，SLAM的计算强度为其增添了额外的复杂性，使其成为该领域既关键又棘手的研究课题。本研究采用粒子滤波SLAM方法应对上述挑战。该方案利用编码数据与光纤陀螺（FOG）信息实现车辆运动的精确估计，同时借助激光雷达技术获取周围障碍物的详细感知信息以增强环境理解能力。通过集成多源数据流，最终构建出粒子滤波SLAM框架——这正是本文旨在有效应对并攻克机器人系统同时定位与建图复杂性的核心研究工作。