SuperPointVO: A Lightweight Visual Odometry based on CNN Feature Extraction

Han, Xiao; Tao, Yulin; Li, Zhuyi; Cen, Ruping; Xue, Fangzheng

doi:10.1109/cacre50138.2020.9230348

Cited by 12 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done by identifying regions of similar color, texture, or other features. Feature extraction involves the technique of detecting and extracting the most critical and relevant characteristics from segmented regions [28], [29], [30]. Feature selection involves the process of selecting the most relevant and informative characteristics from a vast set of features derived from medical images [31], [32], [33].…”

Section: A Cadx Systemsmentioning

confidence: 99%

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

View full text Add to dashboard Cite

Conventional computer-aided diagnosis (CADx) systems play a crucial role in assisting medical professionals with the detection of skin diseases. However, these systems often involve manual, time-consuming, and error-prone processes. Recent studies show that machine learning models have potential to improve the accuracy of CADx systems. In this work, we present research findings aimed at improving the performance of CADx systems for detecting skin diseases by applying ensemble machine learning models. The investigation encompasses the exploration of three popular classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and convolutional neural network (CNN); and an ensemble model of CNN with SVM. The HAM10000 dataset from Kaggle is used to train and test all classification models. Resampling is employed to address class imbalance in the dataset. Through rigorous experiments, the results highlight the compelling efficacy of the ensemble CNNSVM model, unveiling heightened accuracy up to 92% (from CNN accuracy 85% and SVM accuracy 83%). The outcome of this work has profound implications for artificial intelligence (AI) accelerated medical domains in advancing the accuracy and efficiency of skin disease treatment.

show abstract

Section: A Cadx Systemsmentioning

confidence: 99%

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared with traditional VSLAM systems, the robustness of the DXSLAM in different environments has been improved significantly. The SuperPointVO [33] follows the scheme of traditional systems while adopting the SuperPoint [34] to replace the hand-engineered feature extraction, and it achieves a close performance to the advanced VO systems. The LIFT-SLAM [21] employs the Learned Invariant Feature Transform (LIFT) [35] to replace the ORB feature extraction module, and the robustness of the VSLAM system is enhanced.…”

Section: Related Workmentioning

confidence: 99%

A Robust Learned Feature-Based Visual Odometry System for UAV Pose Estimation in Challenging Indoor Environments

Yang

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) are becoming popular nowadays due to their versatility and flexibility for indoor applications, such as the autonomous visual inspection for the inner surface of a pressure vessel. Nevertheless, robust and reliable position estimation is critical for completing these tasks. Visual Odometry (VO) and Visual Simultaneous Localisation and Mapping (VSLAM) allow the UAV to estimate its position in unknown environments. However, traditional feature-based VO/VSLAM systems struggle to deal with complex scenes such as low illumination and textureless environments. Replacing the traditional features with deep learning-based features provides the advantage of handling the challenging environment, but the efficiency is ignored. In this work, an efficient VO system based on a novel lightweight feature extraction network for UAV onboard platforms has been developed. The Deformable Convolution (DFConv) is utilised to improve the feature extraction capability. Owing to the limited onboard computing capability, the Depthwise Separable Convolution (DWConv) is adopted to calculate the offsets for the deformable convolution and construct the backbone network to improve the feature extraction efficiency. Experiments on public datasets indicate that the efficiency of the VO system is improved by 30.03% while preserving the accuracy on embedded platforms with the feature points and descriptors detected by the proposed Convolutional Neural Network (CNN). Moreover, the proposed VO system is verified through UAV flying tests in a real-world scenario. The results prove that the proposed VO system is able to handle the challenging environments where both the latest traditional and deep learning feature-based VO/VSLAM systems fail, and it is feasible for UAV self-localisation and autonomous navigation in the confined, low illumination and textureless indoor environment.

show abstract

“…This approach uses a straightforward homography model as its backend, which generates dynamically. Superpoint is later integrated into a SLAM system [29] and SuperopointVO [30] that demonstrated comparative results compared to traditional SLAM systems.…”

Section: B Slam With Learned Feature Detection and Matchingmentioning

confidence: 99%

UW Deep SLAM-CNN Assisted Underwater SLAM

Amarasinghe,

Ratnaweera,

Maitripala

2023

Applied Computer Systems

View full text Add to dashboard Cite

Underwater simultaneous localization and mapping (SLAM) poses significant challenges for modern visual SLAM systems. The integration of deep learning networks within computer vision offers promising potential for addressing these difficulties. Our research draws inspiration from deep learning approaches applied to interest point detection and matching, single image depth prediction and underwater image enhancement. In response, we propose 3D-Net, a deep learning-assisted network designed to tackle these three tasks simultaneously. The network consists of three branches, each serving a distinct purpose: interest point detection, descriptor generation, and depth prediction. The interest point detector and descriptor generator can effectively serve as a front end for a classical SLAM system. The predicted depth information is akin to a virtual depth camera, opening up possibilities for various applications. We provide quantitative and qualitative evaluations to illustrate some of these potential uses. The network was trained in in several steps, using in-air datasets and followed by generated underwater datasets. Further, the network is integrated into feature-based SALM systems ORBSLAM2 and ORBSSLAM3, providing a comprehensive assessment of its effectiveness for underwater navigation.

show abstract

SuperPointVO: A Lightweight Visual Odometry based on CNN Feature Extraction

Cited by 12 publications

References 22 publications

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

A Robust Learned Feature-Based Visual Odometry System for UAV Pose Estimation in Challenging Indoor Environments

UW Deep SLAM-CNN Assisted Underwater SLAM

Contact Info

Product

Resources

About