Inferring laser-scan matching uncertainty with conditional random fields

Authors:
Zuolei Sun;Joop van de Ven;Fabio Ramos;Xuchu Mao;Hugh Durrant-Whyte
Affiliations:
College of Information Engineering, Shanghai Maritime University, Shanghai, 200135, China;ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, University of Sydney, 2006, NSW, Australia;ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, University of Sydney, 2006, NSW, Australia;School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China;ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, University of Sydney, 2006, NSW, Australia
Venue:
Robotics and Autonomous Systems
Year:
2012

Citing 15
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Estimating uncertain spatial relationships in robotics

Autonomous robot vehicles
Shape registration using optimization for mobile robot navigation

Shape registration using optimization for mobile robot navigation
Globally Consistent Range Scan Alignment for Environment Mapping

Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

Journal of Intelligent and Robotic Systems
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems

Statistics and Computing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Simultaneous localization, mapping and moving object tracking

Simultaneous localization, mapping and moving object tracking
A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Models for learning spatial interactions in natural images for context-based classification

Models for learning spatial interactions in natural images for context-based classification
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

International Journal of Robotics Research
Voronoi random fields: extracting the topological structure of indoor environments via place labeling

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Man-made structure detection in natural images using a causal multiscale random field

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel algorithm for computing robot motion estimates from ranging sensors. The algorithm utilises the recently proposed CRF-Matching procedure which matches laser scans based on shape descriptors. The motion estimates are computed in a sound probabilistic framework by performing inference on a probabilistic graphical model. The Sampling-Product inference algorithm is proposed for obtaining probable association hypothesis from the probabilistic model. The hypothesis are used to generate estimates on the uncertainty of translational and rotational movements of the mobile robot. Experiments demonstrate the benefits of the approach on simulated data sets and on laser scans from an urban environment. The approach is also combined with the well-established delayed-state information filter for a large-scale outdoor simultaneous localisation and mapping task.