Constructing octrees for mobile robots that process continuous point streams in real time poses significant computational and memory challenges. Standard global structures often suffer from high latency and unbalanced tree growth. We introduce the Ego-Centric Octree (ECO), a spatial data structure that acts as a 3D sliding window, dynamically bounding the mapping space to the robot's immediate surroundings. ECO uses an efficient incremental update algorithm that categorizes the environment into shift-out, shift-in, and overlap regions, eliminating redundant global coordinate transformations. Evaluations on the KITTI benchmark demonstrate that ECO reduces update times by up to 25.60% (24.87% on average) compared to full static reconstruction and by up to 67.52% (54.60% on average) compared to a bounded incremental baseline. Furthermore, ECO substantially lowers the total system latency of downstream tasks, running up to 34.17% faster than full reconstruction in voxel-map generation. In dynamic scenes, ECO naturally retains a short-term temporal memory of moving objects, providing useful temporal context while keeping update cost bounded and the tree balanced for real-time spatial perception.
Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR. Existing approaches focus on either egocentric body tracking, estimating the motion of…
Existing approaches to frictional contact dynamics typically either modify the Coulomb law to improve numerical robustness or solve the exact law in a fully coupled monolithic form. However, in its reduced form, exact Coulomb friction can be written as a cone complementarity prob…
Autonomous driving requires both safe and efficient planning decisions in dynamic 3D environments. Although recent Vision/Video-Action models learn policies directly from visual observations and scale well with advances in vision transformers and large-scale training data, they o…
Accurate motion prediction of surrounding agents and safe motion planning are two closely coupled key tasks for social robot navigation in crowded environments. Deploying these systems on resource-constrained edge devices necessitates compact, unified models that can perform both…
Generating safe corridors is essential for collision-free robotic motion planning, yet most existing methods rely on predefined reference paths, which bias corridor geometry and implicitly limit the homotopy classes that can be explored. We propose a reference-path-free corridor…
Safe autonomous navigation in shared environments requires the ability to anticipate and react to the latent behaviors of surrounding robots. In this paper, we propose SOWL-MPC, a safe learning-based predictive control strategy for a novel scenario, which we name ego-world roboti…