scholarly journals Pose Estimation of Omnidirectional Camera with Improved EPnP Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4008
Author(s):  
Xuanrui Gong ◽  
Yaowen Lv ◽  
Xiping Xu ◽  
Yuxuan Wang ◽  
Mengdi Li
Keyword(s):  
Pose Estimation ◽  
Field Of View ◽  
Horizontal Direction ◽  
Light Reflection ◽  
Mirror Surface ◽  
Omnidirectional Camera ◽  
Imaging Model ◽  
Physical Experiments ◽  
Nonlinear Distortions ◽  
Camera Pose

The omnidirectional camera, having the advantage of broadening the field of view, realizes 360° imaging in the horizontal direction. Due to light reflection from the mirror surface, the collinearity relation is altered and the imaged scene has severe nonlinear distortions. This makes it more difficult to estimate the pose of the omnidirectional camera. To solve this problem, we derive the mapping from omnidirectional camera to traditional camera and propose an omnidirectional camera linear imaging model. Based on the linear imaging model, we improve the EPnP algorithm to calculate the omnidirectional camera pose. To validate the proposed solution, we conducted simulations and physical experiments. Results show that the algorithm has a good performance in resisting noise.

scholarly journals POSE ESTIMATION AND MAPPING USING CATADIOPTRIC CAMERAS WITH SPHERICAL MIRRORS

2016 ◽  
Vol XLI-B3 ◽  
pp. 43-47
Author(s):  
Grigory Ilizirov ◽  
Sagi Filin
Keyword(s):  
Pose Estimation ◽  
Field Of View ◽  
Light Reflection ◽  
Control Points ◽  
Mirror Surface ◽  
Small Set ◽  
Catadioptric Cameras ◽  
Object Parts ◽  
Control Configuration ◽  
Spherical Mirrors

Catadioptric cameras have the advantage of broadening the field of view and revealing otherwise occluded object parts. However, they differ geometrically from standard central perspective cameras because of light reflection from the mirror surface which alters the collinearity relation and introduces severe non-linear distortions of the imaged scene. Accommodating for these features, we present in this paper a novel modeling for pose estimation and reconstruction while imaging through spherical mirrors. We derive a closed-form equivalent to the collinearity principle via which we estimate the system’s parameters. Our model yields a resection-like solution which can be developed into a linear one. We show that accurate estimates can be derived with only a small set of control points. Analysis shows that control configuration in the orientation scheme is rather flexible and that high levels of accuracy can be reached in both pose estimation and mapping. Clearly, the ability to model objects which fall outside of the immediate camera field-of-view offers an appealing means to supplement 3-D reconstruction and modeling.

scholarly journals POSE ESTIMATION AND MAPPING USING CATADIOPTRIC CAMERAS WITH SPHERICAL MIRRORS

2016 ◽  
Vol XLI-B3 ◽  
pp. 43-47
Author(s):  
Grigory Ilizirov ◽  
Sagi Filin
Keyword(s):  
Pose Estimation ◽  
Field Of View ◽  
Light Reflection ◽  
Control Points ◽  
Mirror Surface ◽  
Small Set ◽  
Catadioptric Cameras ◽  
Object Parts ◽  
Control Configuration ◽  
Spherical Mirrors

Catadioptric cameras have the advantage of broadening the field of view and revealing otherwise occluded object parts. However, they differ geometrically from standard central perspective cameras because of light reflection from the mirror surface which alters the collinearity relation and introduces severe non-linear distortions of the imaged scene. Accommodating for these features, we present in this paper a novel modeling for pose estimation and reconstruction while imaging through spherical mirrors. We derive a closed-form equivalent to the collinearity principle via which we estimate the system’s parameters. Our model yields a resection-like solution which can be developed into a linear one. We show that accurate estimates can be derived with only a small set of control points. Analysis shows that control configuration in the orientation scheme is rather flexible and that high levels of accuracy can be reached in both pose estimation and mapping. Clearly, the ability to model objects which fall outside of the immediate camera field-of-view offers an appealing means to supplement 3-D reconstruction and modeling.

Initialisation of Autonomous Aircraft Visual Inspection Systems via CNN-Based Camera Pose Estimation

2021 ◽  
Author(s):  
Xueyan Oh ◽  
Leonard Loh ◽  
Shaohui Foong ◽  
Zhong Bao Andy Koh ◽  
Kow Leong Ng ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Visual Inspection ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
Inspection Systems ◽  
Autonomous Aircraft

scholarly journals Augmenting ViSP’s 3D Model-Based Tracker with RGB-D SLAM for 3D Pose Estimation in Indoor Environments

2016 ◽  
Vol XLI-B1 ◽  
pp. 925-932
Author(s):  
J. Li-Chee-Ming ◽  
C. Armenakis
Keyword(s):  
Pose Estimation ◽  
Visual Servoing ◽  
3D Model ◽  
Field Of View ◽  
Indoor Environments ◽  
Camera Motion ◽  
3D Pose Estimation ◽  
Building Models ◽  
Outdoor Environments ◽  
Model Features

This paper presents a novel application of the Visual Servoing Platform’s (ViSP) for pose estimation in indoor and GPS-denied outdoor environments. Our proposed solution integrates the trajectory solution from RGBD-SLAM into ViSP’s pose estimation process. Li-Chee-Ming and Armenakis (2015) explored the application of ViSP in mapping large outdoor environments, and tracking larger objects (i.e., building models). Their experiments revealed that tracking was often lost due to a lack of model features in the camera’s field of view, and also because of rapid camera motion. Further, the pose estimate was often biased due to incorrect feature matches. This work proposes a solution to improve ViSP’s pose estimation performance, aiming specifically to reduce the frequency of tracking losses and reduce the biases present in the pose estimate. This paper explores the integration of ViSP with RGB-D SLAM. We discuss the performance of the combined tracker in mapping indoor environments and tracking 3D wireframe indoor building models, and present preliminary results from our experiments.

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