An Extended HOOFR SLAM Algorithm Using IR-D Sensor Data for Outdoor Autonomous Vehicle Localization

Several works have been carried out in the realm of RGB-D SLAM development, yet they have neither been thoroughly assessed nor adapted for outdoor vehicular contexts. This paper proposes an extension of HOOFR SLAM to an enhanced IR-D modality applied to an autonomous vehicle in an outdoor environment. We address the most prevalent camera issues in outdoor contexts: environments with an image-dominant overcast sky and the presence of dynamic objects. We used a depth-based filtering method to identify outlier points based on their depth value. The method is robust against outliers and also computationally inexpensive. For faster processing, we suggest optimization of the pose estimation block by replacing the RANSAC method used for essential matrix estimation with PROSAC. We assessed the algorithm using a self-collected IR-D dataset gathered by the SATIE laboratory instrumented vehicle using a PC and an embedded architecture. We compared the measurement results to those of the most advanced algorithms by assessing translational error and average processing time. The results revealed a significant reduction in localization errors and a significant gain in processing speed compared to the state-of-the-art stereo (HOOFR SLAM) and RGB-D algorithms (Orb-slam2, Rtab-map).