Precision mapping through the stereo vision and geometric transformations in unknown environments

Petrakis, Georgios; Antonopoulos, Angelos; Tripolitsiotis, Achilles; Trigkakis, Dimitris; Partsinevelos, Panagiotis

doi:10.1007/s12145-023-00972-2

Cited by 4 publications

(6 citation statements)

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“…The evaluation is conducted using the benchmark dataset proposed in (Petrakis & Partsinevelos 2023) designed for planetary and unstructured scenes while the repeatability and homography estimation metrics are utilized for the evaluation of keypoint detection and description respectively.…”

Section: Evaluation and Results Of Superpoint Modelsmentioning

confidence: 99%

“…SuperPoint was implemented using the TensorFlow (Abadi. 2015) deep learning platform and trained utilizing the dataset proposed in (Petrakis & Partsinevelos 2023), aiming to increase the SuperPoint's sensitivity in planetary and unstructured scenes.…”

Section: Superpoint Implementation and Trainingmentioning

confidence: 99%

“…In this study, a SuperPoint neural network (DeTone, 2018) is implemented, optimized and trained in order to accurately conduct feature extraction in unstructured environments, focused on rocky and sandy scenes. For the training process, a dataset of 48 000 images was utilized (Petrakis & Partsinevelos 2023) representing unstructured and planetary scenes from Earth, Moon and Mars. Concerning images from Earth, were captured from construction sites, mountainous areas, sandy beaches and a quarry, while the images from Mars were collected by a publicly available dataset of NASA which includes rover-based images, captured by Mars Science Laboratory (MSL, Curiosity) rover.…”

Section: Introductionmentioning

confidence: 99%

“…Three different models were produced using the aforementioned dataset: (a) an original SuperPoint model, trained from scratch, (b) an original fine-tuned SuperPoint model, (c) an optimized model, trained from scratch. The models were evaluated using a benchmark dataset (Petrakis & Partsinevelos, 2023), designed for unstructured environments including earthy and planetary scenes, aiming to test the accuracy in illumination and viewpoint changes. The experimentation proves that the optimized SuperPoint model provides satisfactory results in keypoint detection and description, compared with the original SuperPoint and popular handcrafted detectors and descriptors.…”

Section: Introductionmentioning

confidence: 99%

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A Superpoint Neural Network Implementation for Accurate Feature Extraction in Unstructured Environments

Petrakis

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Feature extraction plays a crucial role in visual localization, SLAM (Simultaneous Localization and Mapping) and autonomous navigation, by enabling the extraction and tracking of distinctive visual features for both mapping and localization tasks. However, most of the studies, investigate the efficiency and performance of the algorithms in urban, vegetated or indoor environments and not in unstructured environments which suffers by poor information in visual cues where a feature extraction algorithm or architecture could base on. In this study, an investigation of SuperPoint architecture’s efficiency in keypoint detection and description applied to unstructured and planetary scenes was conducted, producing three different models: (a) an original SuperPoint model trained from scratch, (b) an original fine-tuned SuperPoint model, (c) an optimized SuperPoint model, trained from scratch with the same parametarization as the corresponding original model. For the training process, a dataset of 48 000 images was utilized representing unstructured scenes from Earth, Moon and Mars while a benchmark dataset was used aiming to evaluate the model in illumination and viewpoint changes. The experimentation proved that the optimized SuperPoint model provides superior performance using repeatability and homography estimation metrics, compared with the original SuperPoint models, and handcrafted keypoint detectors and descriptors.

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Section: Evaluation and Results Of Superpoint Modelsmentioning

confidence: 99%

Section: Superpoint Implementation and Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Superpoint Neural Network Implementation for Accurate Feature Extraction in Unstructured Environments

Petrakis

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Ruffhead 2021): The scale of the image, The rotation angel of the image axes and Two translations vectors of the image origin(Petrakis, Antonopoulos et al 2023). …”

mentioning

confidence: 99%

Positioning and Detection of Rigid Pavement Cracks Using GNSS Data and Image Processing

Nasrallah,

Abdelfatah,

Attia

et al. 2023

Preprint

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Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. To assess pavement damages conventionally, manual inspection are performed by trained inspectors. This can be time-consuming and a source of risk for inspectors. Moreover, manual inspection can highly affected by the state of mind of inspectors. To overcome such problems, image based inspection using smartphone camera combined with image processing methods can be used. This combination is relatively cheaper and easier to use. This research proposes an automatic crack detection and mapping program for rigid pavement which can automate visual inspection process. The program consists of various image processing techniques that are used to identify and detect cracks from images. Detected cracks are defined in a pixel coordinate system. Cracks coordinates are converted from pixel coordinates to global coordinates in order to compute their lengths using Global Navigation Satellite Systems (GNSSs) data. The performance of the program was assessed with field study. Cracks quantification process is performed to determine crack lengths and areas. The results show that the precision, recall and accuracy values for the program’s image processing algorithm are 57.00%, 98.81% and 65.22% respectively.

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Positioning and detection of rigid pavement cracks using GNSS data and image processing

Nasrallah,

Abdelfatah,

Attia

et al. 2024

Earth Sci Inform

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Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. Manual inspection is performed by trained inspectors to assess pavement damages conventionally. This can be cost-intensive, time-consuming, and a source of risk for inspectors. An image-based inspection using a smartphone is adopted to overcome such problems. This paper proposes an automatic crack detection and mapping program for rigid pavement, which can automate the visual inspection process. The program uses Global Navigation Satellite System (GNSS) data recorded by smartphones and various image processing techniques to detect crack lengths and areas in images. The performance of the program was evaluated by a field study. A crack quantification process was performed to compare the manually measured values and crack lengths obtained from the program. The results show that the program can detect other types of distress, such as pop-outs and punch-outs. This method can achieve satisfactory performance compared to the effort and costs spent.

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Precision mapping through the stereo vision and geometric transformations in unknown environments

Cited by 4 publications

References 50 publications

A Superpoint Neural Network Implementation for Accurate Feature Extraction in Unstructured Environments

A Superpoint Neural Network Implementation for Accurate Feature Extraction in Unstructured Environments

Positioning and Detection of Rigid Pavement Cracks Using GNSS Data and Image Processing

Positioning and detection of rigid pavement cracks using GNSS data and image processing

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