Exploring LiDAR Odometries through Classical, Deep and Inertial perspectives

3D LiDARs have become increasingly popular in the past decade, notably motivated by the safety requirements of autonomous driving requiring new sensor modalities. Contrary to cameras, 3D LiDARs provide direct, and extremely precise 3D measurements of the environment. This has led to the development of many different mapping and Simultaneous Localization And Mapping (SLAM) solutions leveraging this new modality. These algorithms quickly performed much better than their camera-based counterparts, as evidenced by several open-source benchmarks. One critical component ofthese systems is LiDAR odometry. A LiDAR odometry is an algorithm estimating the trajectory of the sensor, given only the iterative integration of the LiDAR measurements. The focus of this work is on the topic of LiDAR Odometries. More precisely, we aim to push the boundaries of LiDAR odometries, both in terms of precision and performance.To achieve this, we first explore classical LiDAR odometries in depth, and propose two novel LiDAR odometries, in chapter 3. We show the strength, and limitations of such methods. Then, to address to improve them we first investigate Deep Learning for LiDAR odometries in chapter 4, notably focusing on end-to-end odometries. We show again the limitations of such approaches and finally investigate in chapter 5 fusing inertial and LiDAR measurements.