Particle Filter SLAM with High Dimensional Vehicle Model

Authors:
David Törnqvist;Thomas B. Schön;Rickard Karlsson;Fredrik Gustafsson
Affiliations:
Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden 581 83;Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden 581 83;Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden 581 83;Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden 581 83
Venue:
Journal of Intelligent and Robotic Systems
Year:
2009

Citing 18
Cited 2

A Flexible New Technique for Camera Calibration

IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
FastSLAM: a factored solution to the simultaneous localization and mapping problem

Eighteenth national conference on Artificial intelligence
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Real-Time Simultaneous Localisation and Mapping with a Single Camera

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Invitation to 3-D Vision: From Images to Geometric Models

An Invitation to 3-D Vision: From Images to Geometric Models
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Fusing Points and Lines for High Performance Tracking

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters

CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Scalable Monocular SLAM

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
MonoSLAM: Real-Time Single Camera SLAM

IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Machine learning for high-speed corner detection

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Particle filters for positioning, navigation, and tracking

IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models

IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems

IEEE Transactions on Signal Processing
Inverse Depth Parametrization for Monocular SLAM

IEEE Transactions on Robotics

SLAM in O(logn) with the Combined Kalman-Information Filter

Robotics and Autonomous Systems
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

Journal of Field Robotics

Quantified Score

Hi-index	0.02

Visualization

Abstract

This work presents a particle filter method closely related to Fastslam for solving the simultaneous localization and mapping (slam) problem. Using the standard Fastslam algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work, an extra factorization of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental data from an unmanned aerial vehicle (helicopter) are presented. The proposed algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro), barometer, and vision in order to solve the slam problem.