Visual-Inertial Teach and Repeat for Aerial Inspection
This addresses the time-consuming and hazardous task of manual inspections for industrial operators, though it is incremental as it builds on existing visual-inertial mapping technology.
The paper tackles the problem of automating visual inspections in industrial facilities by enabling a human operator to teach an inspection path to an autonomous aerial vehicle using a handheld device, resulting in a system that operates robustly in GPS-denied environments with real-time tracking capabilities.
Industrial facilities often require periodic visual inspections of key installations. Examining these points of interest is time consuming, potentially hazardous or require special equipment to reach. MAVs are ideal platforms to automate this expensive and tedious task. In this work we present a novel system that enables a human operator to teach a visual inspection task to an autonomous aerial vehicle by simply demonstrating the task using a handheld device. To enable robust operation in confined, GPS-denied environments, the system employs the Google Tango visual-inertial mapping framework as the only source of pose estimates. In a first step the operator records the desired inspection path and defines the inspection points. The mapping framework then computes a feature-based localization map, which is shared with the robot. After take-off, the robot estimates its pose based on this map and plans a smooth trajectory through the way points defined by the operator. Furthermore, the system is able to track the poses of other robots or the operator, localized in the same map, and follow them in real-time while keeping a safe distance.