MAPPING AND POSITIONING SYSTEM ON OMNIDIRECTIONAL ROBOT USING SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) METHOD BASED ON LIDAR

The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.

3D Environment Exploration with SLAM for Autonomous Mobile Robot Control

In the paper a solution for building of 3D map of unknown terrain for the purposes of control of wheeled autonomous mobile robots operating in an isolated and hard-access area is described. The work environment is represented by a three-dimensional occupancy grid map built with SLAM techniques using LIDAR sensor system. Probabilistic methods such as adaptive Monte Carlo localization and extended Kalman filter are used to concurrently build a map of surroundings and a robot’s pose estimation. A robot’s displacement and orientation are obtained from odometry and inertial navigation system. All algorithms and sub-systems have been implemented and verified with Robot Operation System with a framework for exploration tasks in multi-level buildings

An Extended Analysis on Tuning The Parameters of Adaptive Monte Carlo Localization ROS Package in an Automated Guided Vehicle

With a growth tendency, the employment of the Adaptive Monte Carlo Localization (AMCL) Robot Operational System (ROS) package does not reflect a more indepth discussion on its parameters tuning process. The authors usually do not describe it. This work aims to extend the analysis of the package’s parameters distinct influence in an Automated Guided Vehicle (AGV) indoor localization. The experiments test parameters of the filter, the laser model, and the odometry model. Extending the previous analysis of seven parameters, the present research discusses another ten from the 22 configurable parameters of the package. An external visual vehicle pose tracking is used to compare the pose estimation from the localization package. Although the article does not propose the best parameter tuning, its results discuss how each tested parameter affects the localization. The paper’s contribution is discussing the parameters variation impact on the AGV localization. It may help new researchers in the AMCL ROS package parameter tuning process. The results show minor changes in the default parameters can improve the localization results, even modifying one parameter at a time.

Convergence rates for optimised adaptive importance samplers

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which adapt themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $$\mathcal {O}(1/\sqrt{N})$$ O ( 1 / N ) convergence rate as standard importance samplers, where N is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed optimised adaptive importance samplers (OAIS). These samplers rely on the iterative minimisation of the $$\chi ^2$$ χ 2 -divergence between an exponential family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $$L_2$$ L 2 errors of the optimised samplers converge to the optimal rate of $$\mathcal {O}(1/\sqrt{N})$$ O ( 1 / N ) and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $$L_2$$ L 2 error increases by a factor $$\sqrt{\rho ^\star } > 1$$ ρ ⋆ > 1 , where $$\rho ^\star - 1$$ ρ ⋆ - 1 is the minimum $$\chi ^2$$ χ 2 -divergence between the target and an exponential family proposal.