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 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 Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

2018 ◽  
Vol 8 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Noura Ayadi ◽  
Nabil Derbel ◽  
Nicolas Morette ◽  
Cyril Novales ◽  
Gérard Poisson
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Research Field ◽  
Small Scale ◽  
Mapping Algorithm ◽  
Highly Active ◽  
Localization And Mapping ◽  
Active Research

Abstract In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

TSLAM: Tethered simultaneous localization and mapping for mobile robots

2017 ◽  
Vol 36 (12) ◽  
pp. 1363-1386 ◽  
Author(s):  
Patrick McGarey ◽  
Kirk MacTavish ◽  
François Pomerleau ◽  
Timothy D Barfoot
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Anchor Point ◽  
Outlier Rejection ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

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.

Sign in / Sign up

Export Citation Format

Share Document