Unsupervised learning to detect loops using deep neural networks for visual SLAM system
Published 2015 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Unsupervised learning to detect loops using deep neural networks for visual SLAM system
Authors
Keywords
Simultaneous localization and mapping (SLAM), Loop closure detection, Stacked denoising auto-encoder, Deep neural network
Journal
AUTONOMOUS ROBOTS
Volume 41, Issue 1, Pages 1-18
Publisher
Springer Nature
Online
2015-12-11
DOI
10.1007/s10514-015-9516-2
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Robust RGB-D simultaneous localization and mapping using planar point features
- (2015) Xiang Gao et al. ROBOTICS AND AUTONOMOUS SYSTEMS
- Feature based graph-SLAM in structured environments
- (2014) P. de la Puente et al. AUTONOMOUS ROBOTS
- Learning hierarchical sparse features for RGB-(D) object recognition
- (2014) Liefeng Bo et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Autoencoder for words
- (2014) Cheng-Yuan Liou et al. NEUROCOMPUTING
- A Comparative Study of Registration Methods for RGB-D Video of Static Scenes
- (2014) Vicente Morell-Gimenez et al. SENSORS
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation
- (2013) Mathieu Labbe et al. IEEE Transactions on Robotics
- 3-D Mapping With an RGB-D Camera
- (2013) Felix Endres et al. IEEE Transactions on Robotics
- Robust loop closing over time for pose graph SLAM
- (2013) Yasir Latif et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Multi-resolution surfel maps for efficient dense 3D modeling and tracking
- (2013) Jörg Stückler et al. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
- Topological simultaneous localization and mapping: a survey
- (2013) Jaime Boal et al. ROBOTICA
- Improvement of speeded-up robust features for robot visual simultaneous localization and mapping
- (2013) Yin-Tien Wang et al. ROBOTICA
- Building 3D visual maps of interior space with a new hierarchical sensor fusion architecture
- (2013) Hyukseong Kwon et al. ROBOTICS AND AUTONOMOUS SYSTEMS
- Learning spatially semantic representations for cognitive robot navigation
- (2013) Ioannis Kostavelis et al. ROBOTICS AND AUTONOMOUS SYSTEMS
- Robust Place Recognition With Stereo Sequences
- (2012) C. Cadena et al. IEEE Transactions on Robotics
- Visual SLAM: Why filter?
- (2012) Hauke Strasdat et al. IMAGE AND VISION COMPUTING
- RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
- (2012) Peter Henry et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Feature selection for reliable data association in visual SLAM
- (2012) Zongying Shi et al. MACHINE VISION AND APPLICATIONS
- Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM
- (2011) B. Williams et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Appearance-only SLAM at large scale with FAB-MAP 2.0
- (2010) Mark Cummins et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy
- (2009) Patrick Beeson et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
- A comparative evaluation of interest point detectors and local descriptors for visual SLAM
- (2009) Arturo Gil et al. MACHINE VISION AND APPLICATIONS
- FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
- (2008) K. Konolige et al. IEEE Transactions on Robotics
- FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
- (2008) Mark Cummins et al. INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now