SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words
Jonathan J.Y. Kim, Martin Urschler, Patricia J. Riddle, J\"org S., Wicker

TL;DR
SymbioLCD introduces an ensemble approach combining CNN-extracted objects and visual Bag-of-Words features to improve loop closure detection in SLAM, enhancing robustness, accuracy, and early detection capabilities.
Contribution
The paper presents a novel ensemble-based LCD method that integrates CNN-derived object semantics with vBoW features, outperforming existing ML algorithms in accuracy.
Findings
Outperforms SVM, Decision Tree, Neural Network in LCD accuracy
Utilizes combined CNN and vBoW features for robust candidate prediction
Detects loop closures earlier than current SLAM algorithms
Abstract
Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization. Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against partial occlusions in a scene but cannot perceive the semantics or spatial relationships between feature points. CNN object extraction can address those issues, by providing semantic labels and spatial relationships between objects in a scene. Previous work has mainly focused on replacing vBoW with CNN-derived features. In this paper, we propose SymbioLCD, a novel ensemble-based LCD that utilizes both CNN-extracted objects and vBoW features for LCD candidate prediction. When used in tandem, the added elements of object semantics and spatial-awareness create a more robust and symbiotic loop closure detection system. The proposed SymbioLCD uses…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Advanced Vision and Imaging
MethodsSupport Vector Machine
