EAGER: DG-SLAM: Differential Geometric Simultaneous Localization and Mapping

本计画的研究目标是进一步发展机器人车辆的同步定位与地图建立（SLAM）方法。SLAM是构建周围车辆环境的地图同时相对于正在构建的地图定位车辆的过程。这项工作的动机是合作控制小型，敏捷，空中机器人系统，设想执行任务，如运输和操纵的有效载荷使用夹具或电缆，组装和结构的建设，以及连续的监视和侦察。这些车辆通常使用的传感器受到偏置和高频噪声的破坏。车载计算机的计算能力也有限。一个计算简单但有效的在线SLAM算法，是免疫的偏见和噪声典型的板载传感器是至关重要的自主机器人系统的扩散，和重点提出的研究。该项目侧重于DG-SLAM的理论进步，使其更加实用。具体而言，（a）如何考虑测量偏差，（B）如何直接使用矢量测量，将是拟议的研究的重点。DG-SLAM的这两个扩展对于DG-SLAM在现实世界车辆上的鲁棒性、可靠性和易于实现至关重要。

The research objective of this project is to further the state-of-the-art in simultaneous localization and mapping (SLAM) methods for robotic vehicles. SLAM is the process of building a map of the surrounding vehicle environment while simultaneously localizing the vehicle relative to the map under construction. This work is motivated by cooperative control of small, agile, aerial robotic systems that are envisioned to execute tasks such as the transportation and manipulation of payloads using grippers or cables, assembly and construction of structures, and continuous surveillance and reconnaissance. The sensors these vehicles typically use are corrupted by bias and high-frequency noise. On-board computers are computationally limited as well. A computationally simple yet effective online SLAM algorithm that is immune to bias and noise typical of on-board sensors is crucial to the proliferation of autonomous robotic systems, and the focus of the proposed research. This project focuses on theoretical advancements to DG-SLAM that will make it more practicable. Specifically, (a) how to account for measurement bias, and (b) how to use vector measurements directly, will be the focus of the proposed research. These two extensions of DG-SLAM are critical to the robustness, reliability, and ease of implementation of DG-SLAM on real-world vehicles.