2018/07/19 by Johannes Graeter, Graeter, Johannes, Alexander Wilczynski +3 · 2 citations
Computer Science · Engineering · Environmental Science · #Advanced Vision and Imaging #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote Sensing and LiDAR Applications #Robotics (cs.RO) #Robotics and Sensor-Based Localization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1807.07524
openalex publication_date 2018/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.