IDLS: Inverse Depth Line based Visual-Inertial SLAM

TL;DR

IDLS introduces an inverse depth line representation for visual-inertial SLAM, reducing computational complexity and improving accuracy in challenging indoor environments by efficiently tracking line features.

Contribution

The paper proposes a novel inverse depth line model that simplifies line representation to two variables, enhancing SLAM accuracy and efficiency.

Findings

More accurate line tracking in SLAM

Lower computational overhead compared to existing methods

Robust performance in challenging indoor environments

Abstract

For robust visual-inertial SLAM in perceptually-challenging indoor environments,recent studies exploit line features to extract descriptive information about scene structure to deal with the degeneracy of point features. But existing point-line-based SLAM methods mainly use Pl\"ucker matrix or orthogonal representation to represent a line, which needs to calculate at least four variables to determine a line. Given the numerous line features to determine in each frame, the overly flexible line representation increases the computation burden and comprises the accuracy of the results. In this paper, we propose inverse depth representation for a line, which models each extracted line feature using only two variables, i.e., the inverse depths of the two ending points. It exploits the fact that the projected line's pixel coordinates on the image plane are rather accurate, which partially restrict the line. Using this compact line presentation, Inverse Depth Line SLAM (IDLS) is proposed to track the line features in SLAM in an accurate and efficient way. A robust line triangulation method and a novel line re-projection error model are introduced. And a two-step optimization method is proposed to firstly determine the lines and then to estimate the camera poses in each frame. IDLS is extensively evaluated in multiple perceptually-challenging datasets. The results show it is more accurate, robust, and needs lower computational overhead than the current state-of-the-art of point-line-based SLAM methods.