DisFlow: Scene Flow from Distance Field for Object Pose, Velocity Tracking, and Dynamic Object Reconstruction

We present \emph{DisFlow}, a novel framework for online scene flow estimation from distance field that enables \emph{6DoF dynamic object pose estimation}, \emph{motion tracking}, and \emph{surface reconstruction}. The scene is represented by Gaussian Process Implicit Surfaces (GPIS), with surface normals serving as derivative constraints, enabling accurate signed distance computations near the surface and gradient queries with uncertainty. With this representation as a foundation, we compute a scene flow from the distance field that describes how surface points are transported over time in consecutive frames. Through our flow, we can estimate an object's pose and motion by incrementally registering a new observed point cloud via an elegant closed-form optimisation. Unlike prior methods that operate in the camera or world frame, our approach performs probabilistic fusion directly in the \emph{object frame}, where the object remains geometrically consistent over time. The tight coupling of the DisFlow method in space and time yields dense geometry, surface normals, object pose trajectories, velocities, and uncertainty, all at real-time rates. We evaluate DisFlow on dynamic object sequences and demonstrate that it achieves accurate pose and motion tracking while simultaneously reconstructing high-quality object surfaces. Code publicly available at \href{https://github.com/LanWu076/disflow_ros2}{https://github.com/LanWu076/disflow\_ros2}

Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline

The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. We present a pipeline exploring the perception and "intelligent disobedience" required by such a system. A dataset of two people moving in and out of view has been prepared to compare RGB-based and event-based multi-modal dynamic object detection using LiDAR data for 3D position localisation. Our analysis highlights challenges in accurate 3D localisation using 2D image-LiDAR fusion, indicating the need for further refinement. Compared to the performance of the frame-based detection algorithm utilised (YOLOv4), current cutting-edge event-based detection models appear limited to contextual scenarios, such as for automotive platforms. This is highlighted by weak precision and recall over varying confidence and Intersection over Union (IoU) thresholds when using frame-based detections as a ground truth. Therefore, we have publicly released this dataset to the community, containing RGB, event, point cloud and Inertial Measurement Unit (IMU) data along with ground truth poses for the two people in the scene to fill a gap in the current landscape of publicly available datasets and provide a means to assist in the development of safer and more robust algorithms in the future: https://uts-ri.github.io/revel/.