NDLI: Monocular visual-IMU odometry using multi-channel image patch exemplars

Content Provider	Springer Nature Link
Author	Dong, Xingshuai He, Bo Dong, Xinghui Dong, Junyu
Copyright Year	2016
Abstract	In this paper, we propose three sets of multi-channel image patch features for monocular visual-IMU (Inertial Measurement Unit) odometry. The proposed feature sets extract image patch exemplars from multiple feature maps of an image. We also modify an existing visual-IMU odometry framework by using different salient point detectors and feature sets and replacing the inlier selection approach with a self-adaptive scheme. The modified framework is used to examine the proposed feature sets. In addition to the Root Mean Square Error (RMSE) metric, we use the Hausdorff distance to measure the inconsistency between the estimated and ground-truth trajectories. Compared to the point-wise comparison used by RMSE, the Hausdorff distance takes the shape inconsistency of two trajectories into account and is hence more perceptually consistent. Experimental results show that the multi-channel feature sets outperform, or perform comparably to, the single gray level channel feature sets examined in this study. Particularly, the multi-channel feature set that uses integral channels, i.e., ICIMGP (Integral Channel Image Patches), outperforms two state-of-the-art feature sets: SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). Besides, ICIMGP performs better than the two multi-channel feature sets that are designed based on derivative channels and gradient channels respectively. These promising results are attributed to the fact that the multi-channel features encode richer image characteristics than their single gray level channel counterparts.
Starting Page	11975
Ending Page	12003
Page Count	29
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	76
Issue Number	9
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2016-10-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Visual-IMU odometry Odometry Navigation Multi-channel image features Local features Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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