Visual SLAM: What are the Current Trends and What to Expect?

TL;DR

This paper surveys recent advances in Visual SLAM, highlighting diverse methodologies, datasets, and environments, while discussing current challenges and future research directions in the field.

Contribution

It provides an in-depth literature review of 45 impactful VSLAM papers, classifying them by various characteristics and discussing recent trends and future directions.

Findings

VSLAM methods outperform traditional sensor-based SLAM in many scenarios.

Diverse camera types and datasets are used to evaluate VSLAM systems.

Current challenges include environmental variability and computational efficiency.

Abstract

Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. We can see many research works that demonstrated VSLAMs can outperform traditional methods, which rely only on a particular sensor, such as a Lidar, even with lower costs. VSLAM approaches utilize different camera types (e.g., monocular, stereo, and RGB-D), have been tested on various datasets (e.g., KITTI, TUM RGB-D, and EuRoC) and in dissimilar environments (e.g., indoors and outdoors), and employ multiple algorithms and methodologies to have a better understanding of the environment. The mentioned variations have made this topic popular for researchers and resulted in a wide range of VSLAMs methodologies. In this regard, the primary intent of this survey is to present the recent advances in VSLAM systems, along with discussing the existing challenges and trends. We have given an in-depth literature survey of forty-five impactful papers published in the domain of VSLAMs. We have classified these manuscripts by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. We also discuss the current trends and future directions that may help researchers investigate them.