  K-Nearest Neighbors (KNN) search is a fundamental algorithm in artificial
intelligence software with applications in robotics, and autonomous vehicles.
These wide-ranging applications utilize KNN either directly for simple
classification or combine KNN results as input to other algorithms such as
Locally Weighted Learning (LWL). Similar to binary trees, kd-trees become
unbalanced as new data is added in online applications which can lead to rapid
degradation in search performance unless the tree is rebuilt. Although
approximate methods are suitable for graphics applications, which prioritize
query speed over query accuracy, they are unsuitable for certain applications
in autonomous systems, aeronautics, and robotic manipulation where exact
solutions are desired. In this paper, we will attempt to assess the performance
of non-recursive deterministic kd-tree functions and KNN functions. We will
also present a "forest of interval kd-trees" which reduces the number of tree
rebuilds, without compromising the exactness of query results.
