This paper presents several advances in indoor navigation, multi-agent systems, 3D perception, and 360° imaging for multi-robot human collaboration. First, we proposed a light-assisted dead reckoning (LiDR) system integrating visible light positioning (VLP) and pedestrian dead reckoning (PDR) for high-accuracy indoor localization within 0.7 m average error. VLP provides calibration for PDR drift utilizing light-emitting diode (LED) lights enabled by visible light communication technology. Second, developments in multi-agent reinforcement learning for robotics are explored, emphasizing path planning and collaboration in partially observable environments. The impact of field-of-view settings on communication-based coordination is investigated. For 3D perception, a cross-dimensional refinement methodology is introduced leveraging 2D image features to enhance geometrical details in real-time volumetric reconstruction. This joint 3D geometry and semantic prediction address the limitations of current visual methods. Finally, solutions for calibration and pose estimation are proposed enabling 360° cameras for 3D reconstruction in the perception. Equirectangular projections are converted to cube maps and then aligned via rigid body transformations based on robot location. These innovations in indoor navigation, multi-agent systems, 3D perception, and 360° imaging showcase critical technologies for emerging applications in localization, robotics, and immersive analytics. The methodologies presented provide comprehensive solutions to key challenges in these domains.
Published on International Workshop on Signal Processing and Machine Learning (WSPML), SPIE, 2023.