کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6937770 | 1449838 | 2017 | 30 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment?
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment? Predictive monocular odometry (PMO): What is possible without RANSAC and multiframe bundle adjustment?](/preview/png/6937770.png)
چکیده انگلیسی
Visual odometry using only a monocular camera faces more algorithmic challenges than stereo odometry. We present a robust monocular visual odometry framework for automotive applications. An extended propagation-based tracking framework is proposed which yields highly accurate (unscaled) pose estimates. Scale is supplied by ground plane pose estimation employing street pixel labeling using a convolutional neural network (CNN). The proposed framework has been extensively tested on the KITTI dataset and achieves a higher rank than current published state-of-the-art monocular methods in the KITTI odometry benchmark. Unlike other VO/SLAM methods, this result is achieved without loop closing mechanism, without RANSAC and also without multiframe bundle adjustment. Thus, we challenge the common belief that robust systems can only be built using iterative robustification tools like RANSAC.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 68, December 2017, Pages 3-13
Journal: Image and Vision Computing - Volume 68, December 2017, Pages 3-13
نویسندگان
Nolang Fanani, Alina Stürck, Matthias Ochs, Henry Bradler, Rudolf Mester,