# PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

## TL;DR

PL-SLAM is a stereo visual SLAM system that combines points and line segments to improve robustness in low-textured environments, outperforming existing methods like ORB-SLAM while operating in real-time.

## Contribution

It introduces a novel SLAM system integrating points and line segments throughout all processing stages, including a new bag-of-words loop closure approach, enhancing robustness and map richness.

## Key findings

- Outperforms state-of-the-art methods like ORB-SLAM in robustness.
- Operates in real-time on popular datasets like KITTI and EuRoC.
- Produces richer maps with diverse 3D features.

## Abstract

Traditional approaches to stereo visual SLAM rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in 3D elements, which can be exploited to infer valuable, high-level scene structures like planes, empty spaces, ground plane, etc. (not addressed in this work). Our proposal has been tested with several popular datasets (such as KITTI and EuRoC), and is compared to state of the art methods like ORB-SLAM, revealing a more robust performance in most of the experiments, while still running in real-time. An open source version of the PL-SLAM C++ code will be released for the benefit of the community.