Abstract

We present a SLAM with closed-loop controller method for navigation of NAO humanoid robot from Aldebaran. The method is based on the integration of laser and vision system. The camera is used to recognize the landmarks whereas the laser provides the information for simultaneous localization and mapping (SLAM ). K-means clustering method is implemented to extract data from different objects. In addition, the robot avoids the obstacles by the avoidance function. The closed-loop controller reduces the error between the real position and estimated position. Finally, simulation and experiments show that the proposed method is efficient and reliable for navigation in indoor environments.

Highlights

  • Robot has been used in many areas, such as industry process [1,2,3,4,5,6] and autonomous navigation

  • EKF-simultaneous localization and mapping (SLAM) algorithm was realized in Python code and studied in simulation and experimental implementation

  • The simulation result validated the effort of EKF-SLAM landmark observation in terms of reducing the uncertainty of robot motion

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Summary

Introduction

Robot has been used in many areas, such as industry process [1,2,3,4,5,6] and autonomous navigation. Precise position estimation is a necessary prerequisite for a reliable navigation In this case, simultaneous localization and mapping (SLAM) approach is employed to make a robot truly autonomous without the need for any a priori knowledge of location. In Lin et al.’s paper [14], the proposed approach combined SLAM using a 2D laser scanner and dense 3D depth information from a stereo camera, and this work solved effectively the obstacles at different heights into the 2D map. The objective of this work is to effectively finish accurate position based on SLAM and avoid the obstacle of the environment. The proposed approach combines SLAM using a laser and stereo cameras Using the cameras, it can recognize landmarks and provide obstacle information in the environment.

Laser and Camera Based SLAM with Closed-Loop Controller
Task Scenario
Simulation and Experiment Results
Conclusion and Future Work
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