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

This paper highlights the importance of extracting meaningful information from sensory data in mobile robotics. It introduces a method that combines AI-based object pose estimation from images with inertial measurement unit (IMU) data. This fusion enables accurate multi-object relative state estimation in a 6-DoF context, demonstrated through real-world experiments. The approach’s self-calibrating capabilities ensure reliable and reproducible results.