Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines

Abstract

With the rise of deep learning, there is a fundamental change in visual SLAM algorithms toward developing different modules trained as end-to-end pipelines. However, regardless of the implementation domain, visual SLAM's performance is subject to diverse environmental challenges, such as dynamic elements in outdoor environments, harsh imaging conditions in underwater environments, or blurriness in high-speed setups. These environmental challenges need to be identified to study the real-world viability of SLAM implementations. Motivated by the aforementioned challenges, this paper surveys the current state of visual SLAM algorithms according to the two main frameworks: geometry-based and learning-based SLAM. First, we introduce a general formulation of the SLAM pipeline that includes most of the implementations in the literature. Second, those implementations are classified and surveyed for geometry and learning-based SLAM. After that, environment-specific challenges are formulated to enable experimental evaluation of the resilience of different visual SLAM classes to varying imaging conditions. We address two significant issues in surveying visual SLAM, providing (1) a consistent classification of visual SLAM pipelines and (2) a robust evaluation of their performance under different deployment conditions. Finally, we give our take on future opportunities for visual SLAM implementations.

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Semi-Supervised Classification with Graph Convolutional Networks
• DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• Graph Learning: A Survey
• The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
• DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
• LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method
• AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization
• UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
• Offset equivariant networks and their applications
• Loss it right: Euclidean and Riemannian Metrics in Learning-based Visual Odometry

Cited by in corpus (1)

• Semi-Supervised Pipe Video Temporal Defect Interval Localization