2022/11/07 by Gerhard Kurz, Sebastian A. Scherer, Kurz, Gerhard +6
Computer Science · Engineering · Environmental Science · Mathematics · Physics and Astronomy · #Advanced Optical Sensing Technologies #Aerospace engineering #Artificial intelligence #Computer science #Computer vision #Engineering #FOS: Computer and information sciences #Fuse (electrical) #Geography #Lidar #Light source #Matching (statistics) #Mathematics #Mobile robot #Optics #Physics #Range (aeronautics) #Reflector (photography) #Remote Sensing and LiDAR Applications #Remote sensing #Robot #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Simultaneous localization and mapping #Statistics #cs.RO
paper · pdf · doi:10.48550/arxiv.2211.03484
published in arXiv (Cornell University) (Cornell University) · Accepted at IROS 2022
arxiv created 2022/11/07 · openalex publication_date 2022/11/07 · arxiv updated 2022/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.