i-Octree: A Fast, Lightweight, and Dynamic Octree for Proximity Search

TL;DR

The paper introduces i-Octree, a dynamic octree data structure optimized for real-time proximity search and map updates in robotics, outperforming existing methods in speed and memory efficiency.

Contribution

It presents a novel dynamic octree with local spatial storage and update strategies, enabling fast nearest neighbor search and real-time map modifications.

Findings

Achieves 19% faster runtime on real-world datasets

Supports efficient point insertion, deletion, and down-sampling

Reduces memory usage compared to static and other dynamic trees

Abstract

Establishing the correspondences between newly acquired points and historically accumulated data (i.e., map) through nearest neighbors search is crucial in numerous robotic applications. However, static tree data structures are inadequate to handle large and dynamically growing maps in real-time. To address this issue, we present the i-Octree, a dynamic octree data structure that supports both fast nearest neighbor search and real-time dynamic updates, such as point insertion, deletion, and on-tree down-sampling. The i-Octree is built upon a leaf-based octree and has two key features: a local spatially continuous storing strategy that allows for fast access to points while minimizing memory usage, and local on-tree updates that significantly reduce computation time compared to existing static or dynamic tree structures. The experiments show that i-Octree outperforms contemporary state-of-the-art approaches by achieving, on average, a 19% reduction in runtime on realworld open datasets.