Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning

Chen, Liang; Jin, S.; Xia, Zhoujun

doi:10.3390/s21010310

Cited by 13 publications

(6 citation statements)

References 43 publications

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“…In the closed areas, LIDAR has been the primary sensor for SLAM and robot navigation [26]. Besides, low-cost mono and stereo optic cameras have also been preferred for SLAM under the name of VSLAM [27,28]. Apart from SLAM, detection, and classification of objects through images, videos and live cameras have been attracting so much interest in recent years thanks to its increasing success rate and practical applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Object Detection and Classification Methods For Mobile Robots

Alparslan¹,

Çetin²

2021

Sakarya University Journal of Science

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As one of today's popular research field, mobile robots, are widely used in entertainment, search and rescue, health, military, agriculture and many other fields with the advantages of technological developments. Object detection is one of the methods used for mobile robots to gather and report information about its environment during these tasks. With the ability to detect and classify objects, a robot can determine the type and number of objects around it and use this knowledge in its movement and path planning or reporting the objects with the desired features. Considering the dimensions of mobile robots and weight constraints of flying robots, the use of these algorithms is more limited. While the size and weight of mobile devices should be kept relatively small, successful object classification algorithms require processors with high computational power. In this study, to be able to use object detection information for mapping and path planning, object detection and classification methods were examined, and for the usage in low weight and low energy consuming platforms through developer boards, detection algorithms were compared to each other.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Object Detection and Classification Methods For Mobile Robots

Alparslan¹,

Çetin²

2021

Sakarya University Journal of Science

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“…This method designed an insect-based shallow neural network model without resorting to full deep learning architectures. Chen et al [29] proposed a new multi-constraint loss to optimize the distance constraint relationship in the Euclidean space for efficient CNN model training. In this standard triplet loss method, the tuple composed of three images was used as an input, in which two images were of the same category and the other image was of another different category.…”

Section: Related Workmentioning

confidence: 99%

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

2021

KSII TIIS

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Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

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“…The authors in [14] projected a new hybrid method which generates a higher efficiency primary equal hypothesis generator utilizing short learned sequential descriptor that allows selecting control sequential score aggregation utilizing single image learned descriptor. In [15], a new deep distance learning infrastructure for visual place detection was presented. But in-depth study of several constraints of distance connection from the visual place detection problems, the multi-constraint loss function was presented for optimizing the distance constraint connections from the Euclidean space.…”

Section: Introductionmentioning

confidence: 99%

Seagull Optimization With Deep Learning Driven Condition Invariant Visual Place Recognition Model

Sasikumar¹,

Sathiamoorthy²

2022

INDJCSE

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Visual place recognition (VPR) is most important topic in the computer vision (CV) and robotics community, whereas the task is for efficiently and accurately recognize the place of a provided query image. VPR still remains an open problem because of the several manners whereas the presence of real-world locations can change. As condition-invariant and viewpoint-invariant features were important features to long-term robust visual place-detection, the accomplishment of deep learning (DL) approaches from the CV domain triggered a kind of primary studies as its efficacy for VPR utilizing generic features in networks which are trained for another kind of detection tasks. In this aspect, this article develops a Seagull Optimization with Deep Learning Driven Condition Invariant Visual Place Recognition (SGODL-CIVPR) model. The presented SGODL-CIVPR model majorly involves the recognition of live places with respect to reference images. To accomplish this, the SGODL-CIVPR technique utilizes deep convolutional neural network based EfficientNet model to produce features, and the hyperparameter tuning process takes place using the SGO algorithm. Besides, convolutional wavelet neural network (CWNN) with deep convolutional generative adversarial network (DCGAN) is used to map features to low dimensional space for improving invariant properties and decrease dimensions concurrently. The performance validation of the SGODL-CIVPR model is tested using different datasets and the results demonstrate better performance over other methods.

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Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning

Cited by 13 publications

References 43 publications

Comparison of Object Detection and Classification Methods For Mobile Robots

Comparison of Object Detection and Classification Methods For Mobile Robots

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

Seagull Optimization With Deep Learning Driven Condition Invariant Visual Place Recognition Model

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