Hybrid ICP

TL;DR

Hybrid ICP is a new flexible algorithm that dynamically optimizes data association and error metrics for improved accuracy and robustness in object pose estimation, especially in noisy and sequential camera scenarios.

Contribution

It introduces a novel adaptive ICP variant that optimizes both data association and error metrics in real-time, enhancing performance over traditional fixed-parameter ICP methods.

Findings

Hybrid ICP outperforms standard ICP variants in accuracy.

It demonstrates increased robustness to noise.

The method effectively balances accuracy and computational time in sequential settings.

Abstract

ICP algorithms typically involve a fixed choice of data association method and a fixed choice of error metric. In this paper, we propose Hybrid ICP, a novel and flexible ICP variant which dynamically optimises both the data association method and error metric based on the live image of an object and the current ICP estimate. We show that when used for object pose estimation, Hybrid ICP is more accurate and more robust to noise than other commonly used ICP variants. We also consider the setting where ICP is applied sequentially with a moving camera, and we study the trade-off between the accuracy of each ICP estimate and the number of ICP estimates available within a fixed amount of time.