Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and
  Novel Outliers Detection
Mohamed Afifi Mohamed ElHelw
Perception is a key element for enabling intelligent autonomous navigation.
Understanding the semantics of the surrounding environment and accurate vehicle
pose estimation are essential capabilities for autonomous vehicles, including
self-driving cars and mobile robots that perform complex tasks. Fast moving
platforms like self-driving cars impose a hard challenge for localization and
mapping algorithms. In this work, we propose a novel framework for real-time
LiDAR odometry and mapping based on LOAM architecture for fast moving
platforms. Our framework utilizes semantic information produced by a deep
learning model to improve point-to-line and point-to-plane matching between
LiDAR scans and build a semantic map of the environment, leading to more
accurate motion estimation using LiDAR data. We observe that including semantic
information in the matching process introduces a new type of outlier matches to
the process, where matching occur between different objects of the same
semantic class. To this end, we propose a novel algorithm that explicitly
identifies and discards potential outliers in the matching process. In our
experiments, we study the effect of improving the matching process on the
robustness of LiDAR odometry against high speed motion. Our experimental
evaluations on KITTI dataset demonstrate that utilizing semantic information
and rejecting outliers significantly enhance the robustness of LiDAR odometry
and mapping when there are large gaps between scan acquisition poses, which is
typical for fast moving platforms.