Over the past decade, evolutionary algorithms (EAs) have been introduced to solve range image registration problems because of their robustness and high precision. However, EA-based range image registration algorithms are time-consuming. To reduce the computational time, an EA-based range image registration algorithm using hash map and moth-flame optimization is proposed. In this registration algorithm, a hash map is used to avoid over-exploitation in registration process. Additionally, we present a search equation that is better at exploration and a restart mechanism to avoid being trapped in local minima. We compare the proposed registration algorithm with the registration algorithms using moth-flame optimization and several state-of-the-art EA-based registration algorithms. The experimental results show that the proposed algorithm has a lower computational cost than other algorithms and achieves similar registration precision.
"Range image registration based on hash map and moth-flame optimization," Journal of Electronic Imaging 27(2), 023015 (29 March 2018). https://doi.org/10.1117/1.JEI.27.2.023015
. Submission: Received: 5 September 2017; Accepted: 7 March 2018
Received: 5 September 2017; Accepted: 7 March 2018; Published: 29 March 2018