Edge-Based Loop Closure Detection in Visual SLAM

Mendieta, Darío; Capua, Francisco Raverta; Tarrio, Juan; Moreyra, Marcelo L.

doi:10.23919/aadeca.2018.8577273

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Local feature descriptor methods typically involve extracting distinctive regions or keypoints from images [21], and representing them with descriptors that encode their appearance or local geometry. Image similarity is then measured by comparing these descriptors between images.…”

Section: Related Workmentioning

confidence: 99%

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

Zhu³

2023

Applied Sciences

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This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision.

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Section: Related Workmentioning

confidence: 99%

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

Zhu³

2023

Applied Sciences

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Loop Closure Detection for Visual SLAM Systems Using Various CNN algorithms Contrasts

Lai

Lin

2019

2019 Chinese Automation Congress (CAC)

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MoLO: Drift‐free lidar odometry using a 3D model

Zhao,

Tomko

et al. 2024

The Photogrammetric Record

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LiDAR odometry enables localising vehicles and robots in the environments where global navigation satellite systems (GNSS) are not available. An inherent limitation of LiDAR odometry is the accumulation of local motion estimation errors. Current approaches heavily rely on loop closure to optimise the estimated sensor poses and to eliminate the drift of the estimated trajectory. Consequently, these systems cannot perform real‐time localization and are therefore not practical for a navigation task. This paper presents MoLO, a novel model‐based LiDAR odometry approach to achieve real‐time and drift‐free localization using a 3D model of the environment containing planar surfaces, namely the structural elements of buildings. The proposed approach uses a 3D model of the environment to initialise the LiDAR pose and includes a scan‐to‐scan registration to estimate the pose for consecutive LiDAR scans. Re‐registering LiDAR scans to the 3D model at a certain frequency provides the global sensor pose and eliminates the drift of the trajectory. Pose graphs are built frequently to acquire a smooth and accurate trajectory. A geometry‐based method and a learning‐based method to register LiDAR scans with the 3D model are tested and compared. Experimental results show that MoLO can eliminate drift and achieve real‐time localization while providing an accuracy equivalent to loop closure optimization.

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Edge-Based Loop Closure Detection in Visual SLAM

Cited by 3 publications

References 17 publications

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

Incremental Bag of Words with Gradient Orientation Histogram for Appearance-Based Loop Closure Detection

Loop Closure Detection for Visual SLAM Systems Using Various CNN algorithms Contrasts

MoLO: Drift‐free lidar odometry using a 3D model

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