FISST-SLAM: Finite Set Statistical Approach to Simultaneous Localization and Mapping

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Chen

et al. 2013

Abstract-This paper presents a new multiple vehicle cooperative localization approach based on Random Finite Set (RFS) theory. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors to localize the positions, a solution based on RFS statistics is therefore proposed to consider the whole group behavior instead of each vehicle. For this, we rely on Probability Hypothesis Density (PHD) filtering. Compared to other methods, our approach presents a recursive filtering algorithm that provides dynamic estimation of multiple vehicle states. The proposed method addresses the current challenges in multiple vehicle cooperative localization domain such as communication bandwidth issue, data association uncertainty and the over-convergence problem.A comparative study based on simulations demonstrates the reliability and the feasibility of the proposed approach in large scale environments.

Section: A Overview On Rfs Statisticsmentioning

confidence: 99%

Multiple vehicle cooperative localization under random finite set framework

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Chen

et al. 2013

“…Kalyan [23] and John. M [24] implemented the PHD filter in the field of simultaneous localization and mapping (SLAM) problem.…”

Section: A Overview On Rfs Statisticsmentioning

confidence: 99%

Single camera visual odometry based on Random Finite Set Statistics

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

Gaschler

et al. 2012

Abstract-This paper presents a novel approach based on Random Finite Set (RFS) Statistics for estimating a vehicle's trajectory in complex urban environments by using a fixed single camera. For this, we extend our earlier works which used Probability Hypothesis Density (PHD) filtering under sensor fusion framework and are among the first to apply this technique to visual odometry in real traffic scenes. We consider features acquired from the camera as a group targets, use the PHD filter to update the overall group state and then estimate the ego-motion vector of the camera. Compared to other approaches, our approach presents a recursive filtering algorithm that provides dynamic estimation of multiple-targets states in the presence of clutter and avoids the association problem. Experimental results show that this method provides good robustness under real traffic scenarios.

“…B. Kalyan [24] and John. M [25] implemented the PHD filter in the field of simultaneous localization and mapping (SLAM) problem.…”

Section: A Overview On Rfs Statisticsmentioning

confidence: 99%

Visual odometry based on Random Finite Set Statistics in urban environment

Chen

2012 IEEE Intelligent Vehicles Symposium

et al. 2012

This paper presents a novel approach for estimating the vehicle's trajectory in complex urban environments. In previous work, we presented a visual odometry solution that estimates frame-to-frame motion from a single camera based on Random Finite Set (RFS) Statistics. This paper extends that work by combining the stereo cameras and gyroscope sensor. We are among the first to apply RFS statistics to visual odometry in real traffic scenes. The method is based on two phases: a preprocessing phase to extract features from the image and transform the coordinates from the image space to vehicle coordinates; a tracking phase to estimate the ego-motion vector of the camera. We consider features as a group target and use the Probability Hypothesis Density (PHD) filter to update the overall group state as the motion vector. Compared to other approaches, our method presents a recursive filtering algorithm that provides dynamic estimation of multiple-targets states in the presence of clutter and high association uncertainty. The experimental results show that this method exhibits good robustness under various scenarios.