The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
arXiv:2204.12831 · doi:10.1109/TITS.2022.3175656
Abstract
Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous localization and mapping (SLAM), wherein the robot needs to correctly associate the incoming sensory data to the database allowing consistent map generation. The significant advances in computer vision achieved over the last 20 years, the increased computational power, and the growing demand for long-term exploration contributed to efficiently performing such a complex task with inexpensive perception sensors. In this article, visual loop closure detection, which formulates a solution based solely on appearance input data, is surveyed. We start by briefly introducing place recognition and SLAM concepts in robotics. Then, we describe a loop closure detection system's structure, covering an extensive collection of topics, including the feature extraction, the environment representation, the decision-making step, and the evaluation process. We conclude by discussing open and new research challenges, particularly concerning the robustness in dynamic environments, the computational complexity, and scalability in long-term operations. The article aims to serve as a tutorial and a position paper for newcomers to visual loop closure detection.
arXiv comment25 pages, 15 figures
References in corpus (32)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- ORB-SLAM: a Versatile and Accurate Monocular SLAM System
- Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
- The information bottleneck method
- DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
- Particular object retrieval with integral max-pooling of CNN activations
- Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM
- Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
- Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
- DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
- The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth
- Convolutional Neural Network-based Place Recognition
- VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change
- Semantics for Robotic Mapping, Perception and Interaction: A Survey
- Where is your place, Visual Place Recognition?
- Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods
- Event-based visual place recognition with ensembles of temporal windows
- Deep Feature Space: A Geometrical Perspective
- A Hybrid Compact Neural Architecture for Visual Place Recognition
- The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach
- Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
- HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
- Visual Place Recognition with Probabilistic Vertex Voting
- Condition-Invariant Multi-View Place Recognition
- Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions
- Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments
- Learning Binary Features Online from Motion Dynamics for Incremental Loop-Closure Detection and Place Recognition
- Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description
- Probabilistic Visual Place Recognition for Hierarchical Localization
- Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
- Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment
- Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration
Cited by in corpus (9)
- Visual Place Recognition: A Tutorial
- LiDAR-Based Place Recognition For Autonomous Driving: A Survey
- Neuromorphic Perception and Navigation for Mobile Robots: A Review
- Applications of Spiking Neural Networks in Visual Place Recognition
- A flexible framework for accurate LiDAR odometry, map manipulation, and localization
- Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines
- Ensemble-Based Event Camera Place Recognition Under Varying Illumination
- Addressing the challenges of loop detection in agricultural environments
- InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization