Visual-lidar odometry and mapping: low-drift, robust, and fast
- 1 May 2015
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2174-2181
- https://doi.org/10.1109/icra.2015.7139486
Abstract
Here, we present a general framework for combining visual odometry and lidar odometry in a fundamental and first principle method. The method shows improvements in performance over the state of the art, particularly in robustness to aggressive motion and temporary lack of visual features. The proposed on-line method starts with visual odometry to estimate the ego-motion and to register point clouds from a scanning lidar at a high frequency but low fidelity. Then, scan matching based lidar odometry refines the motion estimation and point cloud registration simultaneously.We show results with datasets collected in our own experiments as well as using the KITTI odometry benchmark. Our proposed method is ranked #1 on the benchmark in terms of average translation and rotation errors, with a 0.75% of relative position drift. In addition to comparison of the motion estimation accuracy, we evaluate robustness of the method when the sensor suite moves at a high speed and is subject to significant ambient lighting changes.Keywords
This publication has 19 references indexed in Scilit:
- Pose Interpolation for Laser‐based Visual OdometryJournal of Field Robotics, 2014
- LOAM: Lidar Odometry and Mapping in Real-timePublished by Robotics: Science and Systems Foundation ,2014
- Efficient Large‐scale Three‐dimensional Mobile Mapping for Underground MinesJournal of Field Robotics, 2014
- Vision meets robotics: The KITTI datasetThe International Journal of Robotics Research, 2013
- Comparing ICP variants on real-world data setsAutonomous Robots, 2013
- River mapping from a flying robot: state estimation, river detection, and obstacle mappingAutonomous Robots, 2012
- RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environmentsThe International Journal of Robotics Research, 2012
- 6D SLAM—3D mapping outdoor environmentsJournal of Field Robotics, 2007
- Two years of Visual Odometry on the Mars Exploration RoversJournal of Field Robotics, 2007
- Visual odometry for ground vehicle applicationsJournal of Field Robotics, 2006