Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICRA.2012.6225135 | 2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) |
Keywords | Field | DocType |
mobile robots,state space,simultaneous localization and mapping,monte carlo methods,visual odometry,sampling methods,markov chain monte carlo,gaussian noise,process model,degree of freedom,uncertainty,motion estimation,markov processes,visualization | Computer vision,Monte Carlo method,Markov process,Visual odometry,Markov chain Monte Carlo,Artificial intelligence,Sampling (statistics),Prior probability,State space,Gaussian noise,Mathematics | Conference |
Volume | Issue | ISSN |
2012 | 1 | 1050-4729 |
Citations | PageRank | References |
2 | 0.40 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jihong Min | 1 | 19 | 3.73 |
| Jungho Kim | 2 | 22 | 3.65 |
| Seunghak Shin | 3 | 13 | 2.07 |
| In So Kweon | 4 | 2795 | 207.62 |