VITAMIN-E: VIsual Tracking and MappINg With Extremely Dense Feature Points

Masashi Yokozuka, Shuji Oishi, Simon Thompson, Atsuhiko Banno; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9641-9650

Abstract


In this paper, we propose a novel indirect monocular simultaneous localization and mapping (SLAM) algorithm called "VITAMIN-E," which is highly accurate and robust as a result of tracking extremely dense feature points. Typical indirect methods have difficulty in reconstructing dense geometry because of their careful feature point selection for accurate matching. Unlike conventional methods, the proposed method processes an enormous number of feature points using the tracking local extrema of curvature based on dominant flow estimation. Because this may lead to high computational cost during bundle adjustment, we propose a novel optimization technique called the "subspace Newton's method" that significantly improves the computational efficiency of bundle adjustment by partially updating the variables. We concurrently generate meshes from the reconstructed points and merge them for an entire three-dimensional(3D) model. Experimental results on the SLAM benchmark EuRoC demonstrated that the proposed method outperformed state-of-the-art SLAM methods such as DSO, ORB-SLAM, and LSD-SLAM, both in terms of accuracy and robustness in trajectory estimation. The proposed method simultaneously generated significantly detailed 3D geometry as a result of the dense feature points in real time using only a CPU.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Yokozuka_2019_CVPR,
author = {Yokozuka, Masashi and Oishi, Shuji and Thompson, Simon and Banno, Atsuhiko},
title = {VITAMIN-E: VIsual Tracking and MappINg With Extremely Dense Feature Points},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}