scholarly journals ESTIMASI POSISI DENGAN MENGGUNAKAN KAMERA MONOKULAR

Transmisi ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 1
Author(s):  
Hadha Afrisal ◽  
Indah Soesanti ◽  
Adha Imam Cahyadi
Keyword(s):  
Mobile Robot ◽  
Pose Estimation ◽  
Simultaneous Localization And Mapping ◽  
Image Sequences ◽  
Multiple View Geometry ◽  
Multiple View ◽  
Localization And Mapping ◽  
Monocular Slam

Pada mobile robot yang memiliki sensor utama hanya berupa kamera, estimasi posisi robot relatif terhadap landmark-landmark visual dapat dilakukan dengan menggunakan pendekatan multiple view geometry. Hal tersebut berguna bagi robot untuk bernavigasi serta melakukan localization dan mapping secara bersamaan. Penelitian ini bertujuan untuk merancang dan mensimulasikan algoritma estimasi posisi yang merupakan langkah awal pada monocular SLAM (Simultaneous Localization and Mapping) 2-dimensi. Penelitian akan membahas tentang fungsi kamera sebagai sensor posisi untuk mobile robot dengan menitikberatkan diskusi pada transformasi citra dengan menggunakan model kamera pinhole dengan parameter intrinsik dan ekstrinsiknya. Berdasarkan hasil pengujian, estimasi pose dan posisi terhadap citra papan catur (checkerboard) berhasil memetakan jarak dan sudut dengan cukup akurat dan dengan error estimasi yang cukup kecil, yakni 2,9 mm untuk estimasi jarak, dan 0.3° untuk estimasi sudut. Simulasi algoritma pose estimation juga telah dilakukan secara offline dengan menggunakan 1200 buah image sequences yang diekstraksi dari citra video.

scholarly journals Mobile Robot Simultaneous Localization and Mapping Based on a Monocular Camera

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Songmin Jia ◽  
Ke Wang ◽  
Xiuzhi Li
Keyword(s):  
Mobile Robot ◽  
Pose Estimation ◽  
Estimation Method ◽  
Simultaneous Localization And Mapping ◽  
Map Building ◽  
Outlier Rejection ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Great Performance ◽  
Feature Based

This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) algorithm for mobile robot. In this proposed method, the tracking and mapping procedures are split into two separate tasks and performed in parallel threads. In the tracking thread, a ground feature-based pose estimation method is employed to initialize the algorithm for the constraint moving of the mobile robot. And an initial map is built by triangulating the matched features for further tracking procedure. In the mapping thread, an epipolar searching procedure is utilized for finding the matching features. A homography-based outlier rejection method is adopted for rejecting the mismatched features. The indoor experimental results demonstrate that the proposed algorithm has a great performance on map building and verify the feasibility and effectiveness of the proposed algorithm.

scholarly journals Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772671
Author(s):  
Jiuqing Wan ◽  
Shaocong Bu ◽  
Jinsong Yu ◽  
Liping Zhong
Keyword(s):  
Mobile Robot ◽  
Posterior Probability ◽  
Belief Propagation ◽  
Nonlinear Models ◽  
Simultaneous Localization And Mapping ◽  
Factor Graph ◽  
Posterior Probability Distribution ◽  
Localization And Mapping ◽  
Nonparametric Techniques ◽  
Non Gaussian

This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot–robot or robot–landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.

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