Hybrid ICP
This work addresses the challenge of enhancing pose estimation accuracy and robustness in computer vision, particularly for applications like robotics or augmented reality, but it appears incremental as it builds on standard ICP frameworks.
The paper tackles the problem of improving object pose estimation by proposing Hybrid ICP, a flexible variant that dynamically optimizes data association and error metrics, resulting in greater accuracy and robustness to noise compared to existing ICP methods.
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.