  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.
