The Experts below are selected from a list of 78054 Experts worldwide ranked by ideXlab platform
Pascal Vasseur - One of the best experts on this subject based on the ideXlab platform.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D–3D registration method for camera synchronization. Once the cameras are synchronized—or for synchronous setups—we also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D–3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D-3D registration method for camera synchronization. Once the cameras are synchronizedor for synchronous setupswe also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement. Keywords
Danda Pani Paudel - One of the best experts on this subject based on the ideXlab platform.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D–3D registration method for camera synchronization. Once the cameras are synchronized—or for synchronous setups—we also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D–3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D-3D registration method for camera synchronization. Once the cameras are synchronizedor for synchronous setupswe also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement. Keywords
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2D-3D camera fusion for visual odometry in outdoor environments
IEEE International Conference on Intelligent Robots and Systems, 2014Co-Authors: Danda Pani Paudel, Perrine Vasseur, Adlane Habed, Cédric Demonceaux, In So KweonAbstract:Accurate estimation of camera Motion is very important for many robotics applications involving SfM and visual SLAM. Such accuracy is attempted by refining the Estimated Motion through nonlinear optimization. As many modern robots are equipped with both 2D and 3D cameras, it is both highly desirable and challenging to exploit data acquired from both modalities to achieve a better localization. Existing refinement methods, such as Bundle adjustment and loop closing, may be employed only when precise 2D-to-3D correspondences across frames are available. In this paper, we propose a framework for robot localization that benefits from both 2D and 3D information without requiring such accurate correspondences to be established. This is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The initial Motion estimation finds the best possible 2D-to-3D correspondences and localizes the cameras with respect the 3D scene. The refinement step minimizes the projection errors of 3D points while preserving the existing relationships between images. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The effect of data inaccuracies is minimized using an M-estimator based technique. Our experiments have demonstrated that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
Adlane Habed - One of the best experts on this subject based on the ideXlab platform.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D–3D registration method for camera synchronization. Once the cameras are synchronized—or for synchronous setups—we also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D–3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D-3D registration method for camera synchronization. Once the cameras are synchronizedor for synchronous setupswe also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement. Keywords
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2D-3D camera fusion for visual odometry in outdoor environments
IEEE International Conference on Intelligent Robots and Systems, 2014Co-Authors: Danda Pani Paudel, Perrine Vasseur, Adlane Habed, Cédric Demonceaux, In So KweonAbstract:Accurate estimation of camera Motion is very important for many robotics applications involving SfM and visual SLAM. Such accuracy is attempted by refining the Estimated Motion through nonlinear optimization. As many modern robots are equipped with both 2D and 3D cameras, it is both highly desirable and challenging to exploit data acquired from both modalities to achieve a better localization. Existing refinement methods, such as Bundle adjustment and loop closing, may be employed only when precise 2D-to-3D correspondences across frames are available. In this paper, we propose a framework for robot localization that benefits from both 2D and 3D information without requiring such accurate correspondences to be established. This is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The initial Motion estimation finds the best possible 2D-to-3D correspondences and localizes the cameras with respect the 3D scene. The refinement step minimizes the projection errors of 3D points while preserving the existing relationships between images. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The effect of data inaccuracies is minimized using an M-estimator based technique. Our experiments have demonstrated that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
Cédric Demonceaux - One of the best experts on this subject based on the ideXlab platform.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D–3D registration method for camera synchronization. Once the cameras are synchronized—or for synchronous setups—we also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D–3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
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2D–3D synchronous/asynchronous camera fusion for visual odometry
Autonomous Robots, 2019Co-Authors: Danda Pani Paudel, Adlane Habed, Cédric Demonceaux, Pascal VasseurAbstract:We propose a robust and direct 2D-3D registration method for camera synchronization. Once the cameras are synchronizedor for synchronous setupswe also propose a visual odometry framework that benefits from both 2D and 3D acquisitions. Our method does not require a precise set of 2D-to-3D correspondences, handles occlusions and works when the scene is only partially known. It is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The results of our experiments demonstrate that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement. Keywords
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2D-3D camera fusion for visual odometry in outdoor environments
IEEE International Conference on Intelligent Robots and Systems, 2014Co-Authors: Danda Pani Paudel, Perrine Vasseur, Adlane Habed, Cédric Demonceaux, In So KweonAbstract:Accurate estimation of camera Motion is very important for many robotics applications involving SfM and visual SLAM. Such accuracy is attempted by refining the Estimated Motion through nonlinear optimization. As many modern robots are equipped with both 2D and 3D cameras, it is both highly desirable and challenging to exploit data acquired from both modalities to achieve a better localization. Existing refinement methods, such as Bundle adjustment and loop closing, may be employed only when precise 2D-to-3D correspondences across frames are available. In this paper, we propose a framework for robot localization that benefits from both 2D and 3D information without requiring such accurate correspondences to be established. This is carried out through a 2D-3D based initial Motion estimation followed by a constrained nonlinear optimization for Motion refinement. The initial Motion estimation finds the best possible 2D-to-3D correspondences and localizes the cameras with respect the 3D scene. The refinement step minimizes the projection errors of 3D points while preserving the existing relationships between images. The problems of occlusion and that of missing scene parts are handled by comparing the image-based reconstruction and 3D sensor measurements. The effect of data inaccuracies is minimized using an M-estimator based technique. Our experiments have demonstrated that the proposed framework allows to obtain a good initial Motion Estimate and a significant improvement through refinement.
Sean N Brennan - One of the best experts on this subject based on the ideXlab platform.
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ego Motion Estimate corruption due to violations of the range flow constraint
Intelligent Robots and Systems, 2018Co-Authors: Chris D Monaco, Sean N BrennanAbstract:Visual odometry methods are increasingly being used to Estimate a vehicle's ego-Motion from range data due to the decreasing cost of range sensors and the impressive speed and accuracy of visual odometry techniques. Dense geometry-based visual odometry methods are fundamentally based on the range flow constraint equation, an equation which depends on the temporal and spatial derivatives of range images. However, these derivatives are calculated with the fundamental assumption that the range flow is magnitude-limited. When scaling this method for faster vehicles, this assumption could be violated, invaliding the range flow constraint equation and thus corrupting the resulting ego-Motion Estimates. This paper derives the sensor, Motion, environment, and sampling frequency conditions that would mathematically violate the range flow constraint. This information is useful for defining the operational limits of dense geometry-based visual odometry methods.
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IROS - Ego-Motion Estimate Corruption Due to Violations of the Range Flow Constraint
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: Chris D Monaco, Sean N BrennanAbstract:Visual odometry methods are increasingly being used to Estimate a vehicle's ego-Motion from range data due to the decreasing cost of range sensors and the impressive speed and accuracy of visual odometry techniques. Dense geometry-based visual odometry methods are fundamentally based on the range flow constraint equation, an equation which depends on the temporal and spatial derivatives of range images. However, these derivatives are calculated with the fundamental assumption that the range flow is magnitude-limited. When scaling this method for faster vehicles, this assumption could be violated, invaliding the range flow constraint equation and thus corrupting the resulting ego-Motion Estimates. This paper derives the sensor, Motion, environment, and sampling frequency conditions that would mathematically violate the range flow constraint. This information is useful for defining the operational limits of dense geometry-based visual odometry methods.