Robot End-Effector Mounted Camera Pose Optimization in Object Detection-Based Tasks

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Loris Roveda ◽  
Marco Maroni ◽  
Lorenzo Mazzuchelli ◽  
Loris Praolini ◽  
Asad Ali Shahid ◽  
...  
Keyword(s):  
Object Detection ◽  
End Effector ◽  
Camera Pose ◽  
Pose Optimization

Online Camera Pose Optimization for the Surround-view System

2019 ◽  
Author(s):  
Xiao Liu ◽  
Lin Zhang ◽  
Ying Shen ◽  
Shaoming Zhang ◽  
Shengjie Zhao
Keyword(s):  
Camera Pose ◽  
Pose Optimization

scholarly journals Summary: Stereo Photogrammetry Camera Pose Optimization.

2020 ◽  
Author(s):  
Bryan Witt ◽  
James Wilbanks ◽  
Brian Owens ◽  
Daniel Rohe
Keyword(s):  
Camera Pose ◽  
Stereo Photogrammetry ◽  
Pose Optimization

scholarly journals JD-SLAM: Joint camera pose estimation and moving object segmentation for simultaneous localization and mapping in dynamic scenes

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199444
Author(s):  
Yujia Zhai ◽  
Baoli Lu ◽  
Weijun Li ◽  
Jian Xu ◽  
Shuangyi Ma
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Object Segmentation ◽  
Three Dimensional ◽  
Simultaneous Localization And Mapping ◽  
Processing Unit ◽  
Dynamic Scenes ◽  
The Real ◽  
Localization And Mapping ◽  
Camera Pose

As a fundamental assumption in simultaneous localization and mapping, the static scenes hypothesis can be hardly fulfilled in applications of indoor/outdoor navigation or localization. Recent works about simultaneous localization and mapping in dynamic scenes commonly use heavy pixel-level segmentation net to distinguish dynamic objects, which brings enormous calculations and limits the real-time performance of the system. That restricts the application of simultaneous localization and mapping on the mobile terminal. In this article, we present a lightweight system for monocular simultaneous localization and mapping in dynamic scenes, which can run in real time on central processing unit (CPU) and generate a semantic probability map. The pixel-wise semantic segmentation net is replaced with a lightweight object detection net combined with three-dimensional segmentation based on motion clustering. And a framework integrated with an improved weighted-random sample consensus solver is proposed to jointly solve the camera pose and perform three-dimensional object segmentation, which enables high accuracy and efficiency. Besides, the prior information of the generated map and the object detection results is introduced for better estimation. The experiments on the public data set, and in the real-world demonstrate that our method obtains an outstanding improvement in both accuracy and speed compared to state-of-the-art methods.

scholarly journals Stereophotogrammetry Camera Pose Optimization

2021 ◽  
pp. 13-38
Author(s):  
Bryan L. Witt ◽  
J. Justin Wilbanks ◽  
Brian C. Owens ◽  
Daniel P. Rohe
Keyword(s):  
Camera Pose ◽  
Pose Optimization

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Object Detection in Cluttered Range Images Using Edgel Geometry

2010 ◽  
Vol 130 (9) ◽  
pp. 1572-1580
Author(s):  
Dipankar Das ◽  
Yoshinori Kobayashi ◽  
Yoshinori Kuno
Keyword(s):  
Object Detection ◽  
Range Images
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