ECO: Incremental Ego-Centric Octree Update for Point Streams

2026-07-06

AI Summary

This work addresses the high latency and structural imbalance of conventional global octrees when processing continuous point cloud streams in real time for mobile robots. To overcome these limitations, the authors propose an Egocentric Octree (ECO) framework that dynamically confines the mapping space to the robot’s local neighborhood and employs a 3D sliding window mechanism. ECO enables efficient incremental updates through region-based classification—categorizing regions as exiting, entering, or overlapping—thereby eliminating redundant global coordinate transformations. The method maintains tree balance while preserving short-term memory of dynamic objects. Experimental results on the KITTI dataset demonstrate that ECO achieves speedups of 24.87% and 54.60% over static reconstruction and baseline incremental approaches, respectively, and accelerates downstream voxel map generation by up to 34.17%.

Abstract

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.

Problem

• octree
• point streams
• real-time
• spatial data structure
• mobile robots

Innovation

• Ego-Centric Octree
• incremental update
• point stream
• real-time spatial perception
• 3D sliding window