HGI-SLAM: Loop Closure With Human and Geometric Importance Features

TL;DR

HGI-SLAM introduces a real-time SLAM system that combines human and geometric features for improved loop closure detection using monocular cameras, outperforming existing methods in robustness and accuracy.

Contribution

The paper presents a novel SLAM approach that merges salient and geometric features into a unified model, enhancing loop closure detection without relying on depth sensors or Lidar.

Findings

Outperforms individual geometric or salient feature methods.

Runs in real-time on monocular cameras.

Robust to large viewpoint changes and organic environments.

Abstract

We present Human and Geometric Importance SLAM (HGI-SLAM), a novel approach to loop closure using salient and geometric features. Loop closure is a key element of SLAM, with many established methods for this problem. However, current methods are narrow, using either geometric or salient based features. We merge their successes into a model that outperforms both types of methods alone. Our method utilizes inexpensive monocular cameras and does not depend on depth sensors nor Lidar. HGI-SLAM utilizes geometric and salient features, processes them into descriptors, and optimizes them for a bag of words algorithm. By using a concurrent thread and combing our loop closure detection with ORB-SLAM2, our system is a complete SLAM framework. We present extensive evaluations of HGI loop detection and HGI-SLAM on the KITTI and EuRoC datasets. We also provide a qualitative analysis of our features. Our method runs in real time, and is robust to large viewpoint changes while staying accurate in organic environments. HGI-SLAM is an end-to-end SLAM system that only requires monocular vision and is comparable in performance to state-of-the-art SLAM methods.