SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments

Authors:
Christoffer Valgren;Achim J. Lilienthal
Affiliations:
AASS Research Centre, Department of Computer Science, Örebro University, SE-70182 Örebro, Sweden;AASS Research Centre, Department of Computer Science, Örebro University, SE-70182 Örebro, Sweden
Venue:
Robotics and Autonomous Systems
Year:
2010

Citing 7
Cited 6

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Localization Based on Building Recognition

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
PCA-SIFT: a more distinctive representation for local image descriptors

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Vision-based global localization and mapping for mobile robots

IEEE Transactions on Robotics

Swarm-supported outdoor localization with sparse visual data

Robotics and Autonomous Systems
A comparative evaluation of feature detectors on historic repeat photography

ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Time-aware co-training for indoors localization in visual lifelogs

MM '11 Proceedings of the 19th ACM international conference on Multimedia
Global localization with non-quantized local image features

Robotics and Autonomous Systems
Dense topological maps and partial pose estimation for visual control of an autonomous cleaning robot

Robotics and Autonomous Systems
Cleaning robot navigation using panoramic views and particle clouds as landmarks

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straightforward method that relies on local image features to compare single-image pairs. We first look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then fine-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The final localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The final localization rate in the single-image matching, cross-seasonal case is between 80% to 95%.