Elastic and efficient LiDAR reconstruction for large-scale exploration tasks
Autor: | Sotiris Papatheodorou, Yiduo Wang, Nils Funk, Maurice Fallon, Stefan Leutenegger, Marco Camurri, Milad Ramezani, Marija Popovic |
---|---|
Přispěvatelé: | Engineering & Physical Science Research Council (EPSRC), SLAMcore Ltd, Engineering & Physical Science Research Council (E |
Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Computer science business.industry cs.RO 3D reconstruction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Mobile robot Frame rate Computer Science - Robotics Lidar Feature (computer vision) Scalability Robot Computer vision Artificial intelligence Focus (optics) business Robotics (cs.RO) |
Zdroj: | ICRA |
Popis: | We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum LiDAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale reconstruction: integration of long-range LiDAR scans at high frequency, the capacity to deform the reconstruction after loop closures are detected, and scalability for long-duration exploration. Our system extends upon a state-of-the-art efficient RGB-D volumetric reconstruction technique, called supereight, to support LiDAR scans and a newly developed submapping technique to allow for dynamic correction of the 3D reconstruction. We then introduce a novel pose graph clustering and submap fusion feature to make the proposed system more scalable for large environments. We evaluate the performance using two public datasets including outdoor exploration with a handheld device and a drone, and with a mobile robot exploring an underground room network. Experimental results demonstrate that our system can reconstruct at 3 Hz with 60 m sensor range and ~5 cm resolution, while state-of-the-art approaches can only reconstruct to 25 cm resolution or 20 m range at the same frequency. Comment: 7 pages, 7 figures |
Databáze: | OpenAIRE |
Externí odkaz: |