Monocular SLAM Supported Object Recognition
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In this work, we develop a monocular SLAMaware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis.By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time.Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis.The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.
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Pillai and Leonard use a monocular SLAM system to boost the performance in the task of object recognition in videos.
“Pillai and Leonard [201] use a monocular SLAM system to boost the performance in the task of object recognition in videos.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [201]
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Feature Detection and Matching With Linear Adjustment and Adaptive Thresholding
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Object Clustering With Dirichlet Process Mixture Model for Data Association in Monocular SLAM
Wei, S., Chen, G., Chi, W., Wang, Z., & Sun, L. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3146553 · Cited by 13 · see citing works
Object discrimination in poultry housing using spectral reflectivity
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Towards real-time segmentation of 3D point cloud data into local planar regions
Tatavarti, A., Papadakis, J., & Willis, A. (2017) · IEEE SoutheastCon · doi:10.1109/secon.2017.7925321 · Cited by 11 · see citing works
SLAM-Supported Self-Training for 6D Object Pose Estimation
Lu, Z., Zhang, Y., Doherty, K., Severinsen, O. A., Yang, Y. E., & Leonard, J. J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981145 · Cited by 10 · see citing works
Self-citationA Unified Framework for Multi-View Multi-Class Object Pose EstimationPreprint
Li, C., Bai, J., & Hager, G. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08103 · Cited by 10 · see citing works
Evaluation of Image Feature Detection and Matching Algorithms
Ou, Y., Cai, Z., Lü, J., Dong, J., & Ling, Y. (2020) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs49078.2020.9118480 · Cited by 9 · see citing works
Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization
Rollo, F., Raiola, G., Zunino, A., Tsagarakis, N., & Ajoudani, A. (2023) · European Conference on Mobile Robots · doi:10.1109/ecmr59166.2023.10256373 · Cited by 8 · see citing works
SceneSLAM: A SLAM framework combined with scene detection
Tong, Z., Shi, D., & Yang, S. (2017) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2017.8324464 · Cited by 7 · see citing works
Coarse Semantic-Based Motion Removal for Robust Mapping in Dynamic Environments
Wang, S., Lv, X., Li, J., & Ye, D. (2020) · IEEE Access · doi:10.1109/access.2020.2989317 · Cited by 7 · see citing works
SLAMinDB: Centralized graph databases for mobile robotics
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Self-citationSemantic Place Understanding for Human–Robot Coexistence—Toward Intelligent Workplaces
Rosa, S., Patanè, A., Lu, C. X., & Trigoni, N. (2018) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2018.2875079 · Cited by 7 · see citing works
Object Recognition from Short Videos for Robotic PerceptionPreprint
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Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous DrivingPreprint
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Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System
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Object-Level Semantic Map Construction for Dynamic Scenes
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Shaped-Based Tightly Coupled IMU/Camera Object-Level SLAM
Hokmabadi, I. A. S., Ai, M., & El‐Sheimy, N. (2023) · Sensors · doi:10.3390/s23187958 · Cited by 6 · see citing works
Incremental multi-view object detection from a moving camera
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Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception?
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Object-Oriented 3D Semantic Mapping Based on Instance Segmentation
Chi, J., Wu, H., & Tian, G. (2019) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2019.p0695 · Cited by 5 · see citing works
An Improved Feature-Based Visual Slam Using Semantic Information
Ma, S., Liang, H., Wang, H., & Xu, T. (2023) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec56291.2023.10082109 · Cited by 5 · see citing works
DEEP LEARNING TO SUPPORT 3D MAPPING CAPABILITIES OF A PORTABLE VSLAM-BASED SYSTEM
Padkan, N., Battisti, R., Menna, F., & Fabio Remondino, A. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-w1-2023-363-2023 · Cited by 5 · see citing works
PRISM: Pose Registration for Integrated Semantic Mapping
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ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM
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PO-SLAM: A Novel Monocular Visual SLAM with Points and Objects
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RGB-D multi-view object detection with object proposals and shape context
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Integrating planning perception and action for informed object search
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VEM-SLAM - Virtual Environment Modelling through SLAM
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Activity scenarios simulation by discovering knowledge through activities of daily living datasets
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Re-localization for Self-Driving Cars using Semantic Maps
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FloorVLoc: A Modular Approach to Floorplan Monocular Localization
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Line–Circle–Square (LCS): A multilayered geometric filter for edge-based detection
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Plane Object-Based High-Level Map Representation for SLAM
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SLAM and Map Learning using Hybrid Semantic Graph Optimization
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A robot navigation method based on human-robot interaction for 3D environment mapping
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Identification of Partially Resolved Objects in Space Imagery with Convolutional Neural Networks
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Spatially-dependent Bayesian semantic perception under model and localization uncertainty
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- Authors
- Pillai, S., & Leonard, J.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- ORCID
- Leonard, John
- Venue
- Robotics: Science and Systems XI
- Published
- 2015-07-13
- OpenAlex
- W1866250967
- Cited by
- 118
- References
- 36
- Citations by year
- 2024: 2 · 2023: 7 · 2022: 8 · 2021: 11 · 2020: 18 · 2019: 15 · 2018: 24 · 2017: 18 · 2016: 14 · 2015: 1
- FWCI
- 32.95
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1318392)
- Multidisciplinary University Research Initiative (N000141110688, N00014-10-1-0936)
- Office of Naval Research (N00014‐10‐1‐0936, MURI N00014-10-1-0936, N00014-11-1-0688, N00014)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- monocular SLAM, object recognition, object detection, feature encoding, RGB camera, real-time recognition
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