A Localization Strategy for Low-cost UAVs Sewers Inspection

Inspecting sewers represents a significant challenge as these environments pose considerable safety risks to human operators. In this view, drones capable of autonomous flight can be used to perform inspection tasks reducing human exposure. However, sewer environments are typically confined, featureless, and poorly lit, hence, standard algorithms for the localization in GNSS-denied environments, such as Visual-Inertial Odometry (VIO), often fail. In addition, drone localization is further complicated by rotor-induced turbulence, and vibrations, that affect sensor measurements. This paper presents a low-cost multisensor-based method for robust pose reconstruction of Unmanned Aerial Vehicles (UAVs) to enable reliable navigation in visually degraded, GPS-denied environments. The proposed framework leverages environmental geometry, specifically obstacle and wall distances, to estimate relative motion and correct drift via a speed control strategy that maximizes the distance from any obstacle. The approach is validated through both simulation and real-world experiments, demonstrating its effectiveness in representative scenarios.