LIMO: Lidar-Monocular Visual Odometry
This addresses the motion estimation challenge in autonomous driving by integrating sensors, though it is incremental as it builds on existing methods.
The paper tackles the problem of visual odometry for autonomous driving by combining LIDAR and monocular camera data, achieving a top-15 placement on the KITTI dataset.
Higher level functionality in autonomous driving depends strongly on a precise motion estimate of the vehicle. Powerful algorithms have been developed. However, their great majority focuses on either binocular imagery or pure LIDAR measurements. The promising combination of camera and LIDAR for visual localization has mostly been unattended. In this work we fill this gap, by proposing a depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment. Semantic labeling is used for outlier rejection and weighting of vegetation landmarks. The capability of this sensor combination is demonstrated on the competitive KITTI dataset, achieving a placement among the top 15. The code is released to the community.