Autonomous robots are increasingly used in search and rescue (SAR) missions. In these missions, lidar sensors are often the most important source of environmental data. However, lidar data can degrade under hazardous conditions, especially when airborne particles such as smoke or dust are present. This degradation can lead to errors in mapping and navigation and may endanger both the robot and humans. Robots therefore need a way to estimate the reliability of their lidar data, so they can make better informed decisions.
This thesis investigates whether anomaly detection methods can be used to quantify lidar data degradation. We apply a semi-supervised deep learning approach called DeepSAD which produces an anomaly score for each lidar scan, serving as a measure of data reliability.
We evaluate this method against baseline methods on an subterranean dataset that includes lidar scans degraded by artificial smoke. Our results show that DeepSAD consistently outperforms the baselines and can clearly distinguish degraded from normal scans. At the same time, we find that the limited availability of labeled data and the lack of robust ground truth remain major challenges. Despite these limitations, our work demonstrates that anomaly detection methods are a promising tool for lidar degradation quantification in SAR scenarios.
