ICC   25427
INSTITUTO DE INVESTIGACION EN CIENCIAS DE LA COMPUTACION
Unidad Ejecutora - UE
artículos
Título:
Visual-inertial teach and repeat
Autor/es:
CIVERA, JAVIER; PESSACG, FACUNDO; NITSCHE, MAT√ćAS
Revista:
ROBOTICS AND AUTONOMOUS SYSTEMS
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Año: 2020 vol. 131
ISSN:
0921-8890
Resumen:
Teach and Repeat (T&R) refers to the technology that allows a robot to autonomously follow a previously traversed route, in a natural scene and using only its onboard sensors. In this paper we present a Visual-Inertial Teach and Repeat (VI-T&R) algorithm that uses stereo and inertial data and targets Unmanned Aerial Vehicles with limited on-board computational resources. We propose a tightly-coupled relative formulation of the visual-inertial constraints that is tailored to the T&R application. In order to achieve real-time operation on limited hardware, we reduce the problem to motion-only visual-inertial Bundle Adjustment. In the repeat stage, we detail how to generate a trajectory and smoothly follow it with a constantly changing relative frame. The proposed method is validated in simulated environments, using real sensor data from the public EuRoC dataset, and using our own robotic setup and closed-loop control. Our experimental results demonstrate high accuracy and real-time performance both on a standard desktop system and on a low-cost Odroid X-U4 embedded computer.