A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding

TL;DR

This paper reviews recent advancements in Visual-SLAM, highlighting the transition from traditional geometric models to modern learning-based semantic scene understanding techniques, and discusses future challenges and opportunities.

Contribution

It provides a comprehensive overview of both geometric and learning-based Visual-SLAM methods, emphasizing recent deep learning approaches and their impact on scene understanding.

Findings

Deep learning enhances Visual-SLAM robustness in challenging environments.

Learning-based methods improve semantic scene understanding in SLAM.

The review identifies key challenges and future research directions.

Abstract

Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided a data-driven approach to tackle the Visual-SLAM problem. This review summarises recent advancements in the Visual-SLAM domain using various learning-based methods. We begin by providing a concise overview of the geometric model-based approaches, followed by technical reviews on the current paradigms in SLAM. Then, we present the various learning-based approaches to collecting sensory inputs from mobile robots and performing scene understanding. The current paradigms in deep-learning-based semantic understanding are discussed and placed under the context of Visual-SLAM. Finally, we discuss challenges and further opportunities in the direction of learning-based approaches in Visual-SLAM.