Flight Test Evaluation of a LIDAR/inertial Navigator for Autonomous Drone Operations in GNSS-denied Urban Environments

This paper focuses on sensor-based navigation solutions for the operation of Uncrewed Aerial Vehicles (UAV) in challenging environments, specifically, low-level flights in urban areas under GNSS-denied (or deteriorated) conditions. A navigation system, based only on high-resolution Light Detection and Ranging (LIDAR) sensors, integrated with an inertial measurement unit (IMU) to achieve accurate and reliable positioning is proposed. The paper covers three different LIDAR-navigation approaches: (1) an absolute positioning method based on Digital Scene Matching (DSM) utilizing point clouds represented by OctoMaps and an a priori known database for global lookup, (2) an odometer-like method that compares OctoMaps from consecutive LIDAR scans for incremental pose estimates without the need for a global map (3) and an odometer-like image-based local pose estimator that uses 2D grayscale heatmaps generated from LIDAR point clouds and optical flow between consecutive images. The algorithms have been tested using flight data collected onboard a heavy-payload, hexacopter UAV equipped with state-of-the-art sensors flown in urban-like environments in Schönwalde, Germany. This paper covers a description of the proposed LIDAR-based navigation methods, the flight test setup, and the evaluation of the methods using performance metrics.