Journal ArticleDOI
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
Mathieu Labbé,François Michaud +1 more
TLDR
This paper presents an online loop-closure detection approach for large-scale and long-term operation based on a memory management method, which limits the number of locations used for loop- closure detection so that the computation time remains under real-time constraints.Abstract:
In appearance-based localization and mapping, loop-closure detection is the process used to determinate if the current observation comes from a previously visited location or a new one. As the size of the internal map increases, so does the time required to compare new observations with all stored locations, eventually limiting online processing. This paper presents an online loop-closure detection approach for large-scale and long-term operation. The approach is based on a memory management method, which limits the number of locations used for loop-closure detection so that the computation time remains under real-time constraints. The idea consists of keeping the most recent and frequently observed locations in a working memory (WM) that is used for loop-closure detection, and transferring the others into a long-term memory (LTM). When a match is found between the current location and one stored in WM, associated locations that are stored in LTM can be updated and remembered for additional loop-closure detections. Results demonstrate the approach's adaptability and scalability using ten standard datasets from other appearance-based loop-closure approaches, one custom dataset using real images taken over a 2-km loop of our university campus, and one custom dataset (7 h) using virtual images from the racing video game “Need for Speed: Most Wanted”.read more
Citations
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Journal ArticleDOI
RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation
Mathieu Labbé,François Michaud +1 more
TL;DR: This paper presents this extended version of RTAB‐Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real‐world datasets, outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.
Proceedings ArticleDOI
Online global loop closure detection for large-scale multi-session graph-based SLAM
Mathieu Labbé,François Michaud +1 more
TL;DR: The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements and is tested and demonstrated using five indoor mapping sessions of a building.
Journal ArticleDOI
Vision-based topological mapping and localization methods
TL;DR: This paper reviews the main solutions presented in the last fifteen years of topological mapping and localization methods, and classify them in accordance to the kind of image descriptor employed, including global, local, BoW and combinations.
Journal ArticleDOI
Unsupervised learning to detect loops using deep neural networks for visual SLAM system
Xiang Gao,Tao Zhang +1 more
TL;DR: The results show that SDA is feasible for detecting loops at a satisfactory precision and can therefore provide an alternative way for visual SLAM systems and show the workflow of training the network, utilizing the features and computing the similarity score.
Proceedings ArticleDOI
Deep learning based wireless localization for indoor navigation
Roshan Ayyalasomayajula,Aditya Arun,Chenfeng Wu,Sanatan Sharma,Abhishek Sethi,Deepak Vasisht,Dinesh Bharadia +6 more
TL;DR: DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches, is presented and augmented with an automated mapping platform, MapFind, which allows off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map.
References
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