Data-driven MCMC sampling for vision-based 6D SLAM

Abstract

An efficient sampling technique for estimating camera motion is presented. For this purpose, Markov chain Monte Carlo (MCMC) sampling is incorporated into the data-driven proposal distribution in order to improve the SLAM performance. Experimental results using both synthetic and real datasets demonstrate the efficiency of the proposed method.