Efficient Data-Driven MCMC sampling for vision-based 6D SLAM

In this paper, we propose a Markov Chain Monte Carlo (MCMC) sampling method with the data-driven proposal distribution for six-degree-of-freedom (6-DoF) SLAM. Recently, visual odometry priors have been widely used as the process model in the SLAM formulation to improve the SLAM performance. However, modeling the uncertainties of incremental motions estimated by visual odometry is especially difficult under challenging conditions, such as erratic motion. For a particle-based model representation, it can represent the uncertainty of the camera motion well under erratic motion compared to the constant velocity model or a Gaussian noise model, but the manner of representing the proposal distribution and sampling the particles is extremely important, as we can maintain only a limited number of particles in the high-dimensional state space. Hence, we propose an effective sampling approach by exploiting MCMC sampling and the data-driven proposal distribution to propagate the particles. We demonstrate the performance of the proposed approach for 6-DoF SLAM using both synthetic and real datasets and compare the performance with those of other sampling methods.

Authors

• Jihong Min
• Jungho Kim 0005
• Seunghak Shin
• In-So Kweon

Keywords

• Proposals
• Cameras
• Simultaneous localization and mapping
• Uncertainty
• Standards
• Sampling methods
• Visualization
• Markov Chain Monte Carlo
• Sampling Method
• High-dimensional
• Process Model
• Markov Chain
• Challenging Conditions
• Camera Motion
• Particles In Space
• Visual Odometry
• Gaussian Noise Model
• High-dimensional State Space
• Constant Velocity Model
• Performance Of Method
• Average Error
• Control Input
• Graphics Processing Unit
• Indoor Environments
• Left Image
• Motion Estimation
• Camera Pose
• False Matches
• Stereo Camera
• Metropolis-Hastings
• Particle Weight
• SIFT Features
• Extended Kalman Filter
• Zero-mean Gaussian Noise
• Probabilistic Formulation
• Image Coordinates