Instance Invariant Visual Servoing Framework for Part-Aware Autonomous Vehicle Inspection using MAVs


Visual servoing approaches navigate the robot to a desired pose with respect to the given object using image measurements. As a result these approaches have several applications in manipulation, navigation and inspection. However, existing visual servoing approaches are instance specific i.e. they control camera motion between two views of the same object. In this paper, we present a framework for visual servoing to a novel object instance. We further employ our framework for autonomous inspection of vehicles using Micro Aerial Vehicles (MAVs), which is vital for day-to-day maintenance, damage assessment and merchandising a vehicle. This visual inspection task comprises of the MAV visiting the essential parts of the vehicle, for example wheels, lights, etc., to get a closer look at the damages incurred. Existing methods for autonomous inspection could not be extended for vehicles due to following reasons: Firstly, several existing methods require a 3D-model of the structure, which is not available for every vehicle. Secondly, existing methods require expensive depth-sensor for localization and path planning. Thirdly, current approaches do not account for semantic understanding of the vehicle, which is essential for identifying parts. Our instance invariant visual servoing framework is capable of autonomously navigating to every essential part of a vehicle for inspection and can be initialized from any random pose. To the best our knowledge this is the first approach demonstrating fully autonomous visual inspection of vehicles using MAVs. We have validated the efficacy of our approach through a series of experiments in simulation and outdoor scenarios.


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