A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

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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 mars volta richmond 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 mz7lh3t8hmlt 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.