Details

Autor(en) / Beteiligte

• Jantos, Thomas
• Brommer, Christian
• Allak, Eren
• Weiss, Stephan
• Steinbrener, Jan

Titel

AI-Based Multi-Object Relative State Estimation with Self-Calibration Capabilities

Ist Teil von

• 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, p.2789-2795

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of critical infrastructure with Unmanned Aerial Vehicles (UAVs), for example, requires precise navigation relative to the structure that is to be inspected. Recently, Artificial Intelligence (AI)-based methods have been shown to excel at extracting semantic information such as 6 degree-of-freedom (6-DoF) poses of objects from images. In this paper, we propose a method combining a state-of-the-art AI-based pose estimator for objects in camera images with data from an inertial measurement unit (IMU) for 6-DoF multi-object relative state estimation of a mobile robot. The AI-based pose estimator detects multiple objects of interest in camera images along with their relative poses. These measurements are fused with IMU data in a state-of-the-art sensor fusion framework. We illustrate the feasibility of our proposed method with real world experiments for different trajectories and number of arbitrarily placed objects. We show that the results can be reliably reproduced due to the self-calibrating capabilities of our approach.