Data transferring model determination in robotic group

Sergiyenko, Oleg; Иванов, М. П.; Tyrsa, Vera; Карташов, В. М.; Rivas-López, Moisés; Hernández-Balbuena, Daniel; Flores-Fuentes, Wendy; Rodríguez-Quiñonez, Julio C.; Hipólito, Juan Iván Nieto; Hernández, Wilmar; Tchernykh, Andrei

doi:10.1016/j.robot.2016.04.003

Cited by 62 publications

(18 citation statements)

References 21 publications

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“…To improve the sky-view image segmentation accuracy, we will adopt the deep learning-based image segmentation method to enhance the satellite classification method. Moreover, we will study the integration of GNSS and INS [3] by using the proposed NLOS correction method to find out the potential of the proposed method in GNSS/INS fusion for autonomous systems [2].…”

Section: Discussionmentioning

confidence: 99%

“…Insufficient positioning accuracy [1] in urban canyon areas is still one of the key problems that postpone the arrival of the large population of autonomous systems [2]. Light detection and ranging (LiDAR), camera, and inertial navigation system (INS) [3] are usually integrated with global navigation satellite system (GNSS) positioning [4][5][6][7] to obtain robust positioning.…”

Section: Introductionmentioning

confidence: 99%

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Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai

Wen

Hsu

2020

IET Intelligent Transport Systems

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai

Wen

Hsu

2020

IET Intelligent Transport Systems

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“…Model Predictive Control [23], or UAV so the control input be correctly applied and the Visual Path Follower be evaluated in more challenging situations. As a perspective for indoor navigation, the scene model might include obstacles mapped from a group of robots [24] prior to the process of the Photometric-Planner.…”

Section: Discussionmentioning

confidence: 99%

Photometric-Planner for Visual Path Following

et al. 2021

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…1 The development of advanced driver assistance systems (ADAS) can improve driving safety. 2,3 Several ADAS, such as the lane departure warning system, 4 lane keeping system, 5 target localization, 6 and obstacle avoidance, 7 have been gradually turned into commercial products. 8 However, existing systems are still far from satisfaction, especially under challenging scenarios.…”

Section: Introductionmentioning

confidence: 99%

Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

Huang

Liu

2021

International Journal of Advanced Robotic Systems

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The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.

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Data transferring model determination in robotic group

Cited by 62 publications

References 21 publications

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Photometric-Planner for Visual Path Following

Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

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