Robust multi-robot localization and mapping

强大的多机器人定位和地图绘制

• 批准号: 549159-2019
• 负责人: Beltrame, Giovanni
• 金额: $ 2.19万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=701079
• 关键词: Robust; multi; robot; localization; mapping

SLAM (Simultaneous Localization And Mapping) has been an extremely active and fast-evolving field for the past decades. It attempts to solve the fundamental problem in truly autonomous systems: how to extract information from data and fuse the information to create a map of the environment, which facilitates the subsequent decision making process. Despite the extensive works and vast advances, long-term operations remain an open problem due to the brittleness of current SLAM systems. In particular, perceptual aliasing, where different places appear similar to the sensor, prevents ``loop closure detection'', i.e. recognizing a place that was already visited, as well as map estimation from achieving reliable performance over long periods of time. This project aims at studying novel ways to perform distributed SLAM and solve the aforementioned problems. At the end of the project, we expect to have new algorithms for sensor fusion, as well as a distributed SLAM solution that includes synchronization and map merging. This system will be integrated in our partner's set of products and used for accurate mapping of environments without GPS (indoor, caves, forests, etc). The project will contribute to a product for SLAM that will provide state-of-the-art solutions for Canada's drone and robotics industry in general, with a direct effect on the Canadian economy.

SLAM（同步定位与建图）在过去几十年中一直是一个极其活跃且快速发展的领域。它试图解决真正自治系统中的根本问题：如何从数据中提取信息并融合这些信息以创建环境地图，从而方便后续的决策过程。尽管进行了大量的工作并取得了巨大的进步，但由于当前 SLAM 系统的脆弱性，长期运行仍然是一个悬而未决的问题。特别是，感知混叠（不同的地方看起来与传感器相似）会阻止“闭环检测”，即识别已经访问过的地方，以及地图估计无法在长时间内实现可靠的性能。 该项目旨在研究执行分布式 SLAM 的新方法并解决上述问题。在项目结束时，我们期望有新的传感器融合算法，以及包括同步和地图合并的分布式 SLAM 解决方案。该系统将集成到我们合作伙伴的产品集中，用于在没有 GPS 的情况下精确绘制环境地图（室内、洞穴、森林等）。 该项目将有助于开发 SLAM 产品，为加拿大的无人机和机器人行业提供最先进的解决方案，对加拿大经济产生直接影响。