Comparison of Point and Line Features and Their Combination for Rigid Body Motion Estimation

Authors:
Florian Pilz;Nicolas Pugeault;Norbert Krüger
Affiliations:
Department of Medialogy and Engineering Science, Aalborg University Copenhagen, Denmark;School of Informatics, University of Edinburgh, United Kingdom and The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark;The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark
Venue:
Statistical and Geometrical Approaches to Visual Motion Analysis
Year:
2009

Citing 0
Cited 4

Spatial-Temporal Junction Extraction and Semantic Interpretation

ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Using multi-modal 3D contours and their relations for vision and robotics

Journal of Visual Communication and Image Representation
Temporal accumulation of oriented visual features

Journal of Visual Communication and Image Representation
Accumulation of different visual feature descriptors in a coherent framework

SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses the usage of different image features and their combination in the context of estimating the motion of rigid bodies (RBM estimation). From stereo image sequences, we extract line features at local edges (coded in so called multi-modal primitives) as well as point features (by means of SIFT descriptors). All features are then matched across stereo and time, and we use these correspondences to estimate the RBM by solving the 3D-2D pose estimation problem. We test different feature sets on various stereo image sequences, recorded in realistic outdoor and indoor scenes. We evaluate and compare the results using line and point features as 3D-2D constraints and we discuss the qualitative advantages and disadvantages of both feature types for RBM estimation. We also demonstrate an improvement in robustness through the combination of these features on large data sets in the driver assistance and robotics domain. In particular, we report total failures of motion estimation based on only one type of feature on relevant data sets.