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.

翻译：自主驾驶的高级功能在很大程度上取决于对车辆的精确运动估计,已经开发出强大的算法,但是,它们绝大多数侧重于望远镜图像或纯粹的LIDAR测量。摄影机和LIDAR视觉定位的有希望的组合大多没有人参加。在这项工作中,我们通过提议从LIDAR的摄影特征轨迹测量中提取深度算法来填补这一空白,并通过基于Bundle 调整的坚固键框架来估计运动。语义标签用于排除和加权植被标志。这种传感器组合的能力在竞争性的KITTI数据集上得到了证明,在15位顶级数据集中找到了位置。该代码向社区开放。