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RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping.
Xin, Rui; Guo, Ningyan; Ma, Xingyu; Liu, Gang; Feng, Zhiyong.
Afiliación
  • Xin R; Department of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100874, China.
  • Guo N; Department of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100874, China.
  • Ma X; Department of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100874, China.
  • Liu G; Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
  • Feng Z; Department of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100874, China.
Sensors (Basel) ; 24(17)2024 Aug 31.
Article en En | MEDLINE | ID: mdl-39275582
ABSTRACT
Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza