Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or LiDARs. In this work, we present a resilient approach that exploits the redundancy of multiple odometry algorithms using a 3D LiDAR scanner and a monocular camera to provide reliable state estimation for autonomous vehicles. Our system utilizes a stack of odometry algorithms that run in parallel. It chooses from them the most promising pose estimation considering sanity checks using dynamic and kinematic constraints of the vehicle as well as a score computed between the current LiDAR scan and a locally built point cloud map. In this way, our method can exploit the advantages of different existing ego-motion estimating approaches. We evaluate our method on the KITTI Odometry dataset. The experimental results suggest that our approach is resilient to failure cases and achieves an overall better performance than individual odometry methods employed by our system.

翻译：强力和可靠的自我感动是大多数自主移动系统的关键组成部分。 许多odoricat 估计方法是使用摄影机或LiDARs等不同传感器开发的。 在这项工作中,我们展示了一种弹性方法,利用3D LiDAR扫描仪和单镜照相机的冗余多种odoric 算法,为自主车辆提供可靠的状态估计。我们的系统使用一系列平行运行的odoricat 算法。它从中选择了最有希望的估计,其中考虑到使用机动车的动态和运动限制进行理智检查,以及当前LiDAR扫描和当地建造的点云图之间的得分。这样,我们的方法可以利用现有不同自我感动估计方法的优势。我们用KITTI Odoricat 数据集评估了我们的方法。实验结果表明,我们的方法能够抵御失败案例,并取得比我们系统使用的个体odorig方法更好的总体性能。