Vsevolod Hulchuk, MSc.

In this paper, we address robot localization using Simultaneous Localization and Mapping (SLAM) with Light Detection and Ranging (LiDAR) perception enhanced by visual odometry in scenarios where laser scan matching can be ambiguous because of a lack of sufficient features in the scan. We propose a Graph-based SLAM approach that benefits from fusing data from multiple types of sensors to overcome the disadvantages of using only LiDAR data for localization. The proposed method uses a failure detection model based on the quality of the LiDAR scan matching and inertial measurement unit data. The failure model improves LiDAR-based localization by an additional localization source, including low-cost blackbox visual odometers like the Intel RealSense T265. The proposed method is compared to the state-of-the-art localization system LIO-SAM in cluttered and open urban areas. Based on the performed experimental deployments, the proposed failure detection model with black-box visual odometry sensor yields improved localization performance measured by the absolute trajectory and relative pose error indicators.