Comparing Vision-Based Monte-Carlo Localization Methods

Almeida, Aislan C.; Neto, Sylvio Rubens Juliani; Bianchi, Reinaldo A. C.

doi:10.1109/lars/sbr/wre.2018.00084

Cited by 4 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the ball's angle value towards the x refers point axis is calculated. Finally, the angle is determined using the trigonometry invers method [35], such as (5). The angle value is written in radian value, so the degree value input must be converted to a radian value.…”

Section: Estimation Functionmentioning

confidence: 99%

“…Second, when Cy value is above y value center of image variable (Cy > 539). Both of the condition gives two different results using if are calculated using (5). Equation ( 6) is provided to encounter this problem.…”

Section: = Acos ( )mentioning

confidence: 99%

“…The generated result by using this method is a map. Then this method would generate a better result in an indoor area [5], [6]. Meanwhile, this method requires a high capability computation hardware and the use of appropriate sensors such as LiDAR [7], [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Object Position Estimation based on Dual Sight Perspective Configuration

et al. 2021

View full text Add to dashboard Cite

Development of the coordination system requires the dataset because the dataset could provide information around the system that the coordination system can use to make decisions. Therefore, the capability to process and display data-related positions of objects around the robots is necessary. This paper provides a method to predict an object’s position. This method is based on the Indoor Positioning System (IPS) idea and object position estimation with the multi-camera system (i.e., stereo vision). This method needs two input data to estimate the ball position: the input image and the robot’s relative position. The approach adopts simple and easy calculation technics: trigonometry, angle rotations, and linear function. This method was tested on a ROS and Gazebo simulation platform. The experimental result shows that this configuration could estimate the object’s position with Mean Squared Error was 0.383 meters. Besides, R squared distance calibration value is 0.9932, which implies that this system worked very well at estimating an object’s position.

show abstract

Section: Estimation Functionmentioning

confidence: 99%

Section: = Acos ( )mentioning

confidence: 99%

See 1 more Smart Citation

Object Position Estimation based on Dual Sight Perspective Configuration

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The comparison of localization strategies is essential in the field of mobile robotics because the results they yield enable users to choose one method over others. In the literature, there are reported comparisons of localization strategies that demonstrate the accuracy of the tested algorithms, as shown in (Fauser et al, 2017) (Almeida et al, 2018). On the other hand, (Esan et al, 2020) provides a review of navigation systems in mobile robots, and it also mentions some of the most commonly used localization methods in these systems, which are classified based on the type of localization they offer, i.e., relative or absolute.…”

Section: Introductionmentioning

confidence: 99%

Comparisson between generalized geometric triangulation and odometry

Mar-Castro,

Aparicio-Lastiri,

Pérez-Arista

et al. 2024

ICBI

View full text Add to dashboard Cite

The localization in mobile robotics is essential for carrying out autonomous tasks. For this reason, different algorithms have been developed to estimate the robot's pose, either relatively or absolutely. One of the best known is Wheel-based Odometry, which is easy to implement but the error tends to increase with respect to time producing an unreliable estimation. In contrast, absolute localization algorithms such as Generalized Geometric Triangulation (GGT) offer higher accuracy, although their implementation may require more advanced measurement systems, and pose estimation can be slow. This work compares these two algorithms and shows what happens with the pose estimation when used for period of time. The presented results were obtained in a real testing area equipped with a Turtlebot3 model burger robot.

show abstract