Robust Navigation in Off-Road Terrain Using Radar : Development SLAM framework that leverages radar technology to enable navigation in off-road conditions

Sammanfattning

Harsh and unstructured environments such as underground tunnels, forests, or extraterrestrial terrains pose significant challenges for autonomous vehi-cle navigation, especially where traditional positioning systems like GNSS are unreliable and vision or LiDAR-based sensors fail due to poor visibility conditions.In such scenarios, radar emerges as a robust alternative, capable of operat-ing in darkness, fog, or dust. However, radar-based simultaneous localisation and mapping (SLAM) faces unique limitations, including its primarily 2D data output and therefore limited vertical resolution and usability.This thesis addresses these challenges by developing a robust radar-based SLAM framework that enables autonomous navigation in off-road conditions. The approach leverages a Navtech RAS3 radar in combination with other sen-sors, and commercial off-the-shelf hardware components. A novel image pro-cessing method is proposed to create a "2.5-dimensional stacked terrain model" using 2D radar data, exploiting a technique referred to as “terrain-induced beam steering”.The framework integrates sensor fusion algorithms implemented in the robot operating system (ROS), combined with an odometry estimation via the “Con-servative Filtering for Efficient and Accurate Radar” (CFEAR) algorithm.Preliminary test results, including static, tunnel, and off-road experiments, demonstrate the technical feasibility of the integrated sensor suite and the implemented radar-centric SLAM framework. While full consistency in odom-etry and high-quality mapping were not fully achieved, the system success-fully performs sensor fusion, angle dependent radar image correction, and initial odometry estimation.This work lays the foundation for more advanced and fully autonomous ra-dar-based navigation systems, particularly in visibility-impaired or GNSS-de-nied environments. The developed framework serves as a robust baseline for future iterations focusing on long-term autonomy, improved loop closure, and higher map fidelity in complex off-road scenarios.