ikd-Tree: An Incremental K-D Tree for Robotic Applications

TL;DR

The paper introduces ikd-Tree, an incremental k-d tree data structure optimized for robotic applications, enabling faster dynamic space partitioning and efficient nearest neighbor searches through incremental updates and parallel processing.

Contribution

The novel ikd-Tree supports incremental updates, partial re-balancing, and multi-threading, significantly improving efficiency over static k-d trees in robotic tasks.

Findings

Consumes only 4% of the runtime of static k-d trees in tests.

Proven to be theoretically efficient with a complete time complexity analysis.

Validated on real-world LiDAR data with high performance in odometry and mapping.

Abstract

This paper proposes an efficient data structure, ikd-Tree, for dynamic space partition. The ikd-Tree incrementally updates a k-d tree with new coming points only, leading to much lower computation time than existing static k-d trees. Besides point-wise operations, the ikd-Tree supports several features such as box-wise operations and down-sampling that are practically useful in robotic applications. In parallel to the incremental operations (i.e., insert, re-insert, and delete), ikd-Tree actively monitors the tree structure and partially re-balances the tree, which enables efficient nearest point search in later stages. The ikd-Tree is carefully engineered and supports multi-thread parallel computing to maximize the overall efficiency. We validate the ikd-Tree in both theory and practical experiments. On theory level, a complete time complexity analysis is presented to prove the high efficiency. On experiment level, the ikd-Tree is tested on both randomized datasets and real-world LiDAR point data in LiDAR-inertial odometry and mapping application. In all tests, ikd-Tree consumes only 4% of the running time in a static k-d tree.