# IDLS: Inverse Depth Line based Visual-Inertial SLAM

arXiv: https://arxiv.org/abs/2304.11748 · HTML: https://mlsanity.com/paper/2304.11748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 58 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence](https://mlsanity.com/paper/2006.12567/) (2020)
- [PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features](https://mlsanity.com/paper/2009.07462/) (2020)

## Built on by (most cited first, 1 of 1)

- [Unified and Efficient Point-Line Local Features](https://mlsanity.com/paper/2608.19894/) (2026)

## Abstract (arXiv)

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.