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SLAM with EKF implementation in embedded system with 32 bits microcontroller

Grant number: 14/07549-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): June 01, 2014
Effective date (End): December 31, 2014
Field of knowledge:Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
Principal researcher:Marcelo Nicoletti Franchin
Grantee:Gleydson Ferreira Neves
Home Institution: Faculdade de Engenharia (FE). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil

Abstract

Simultaneous localization and mapping (SLAM) is a process in which a mobile robot can build an environment map where it is located and meanwhile use this map to calculate its location, using its sensors, for the obstacles distances measurements, objects and landmarks detection, odometry, environment images, etc. The big majority of the surveys in this topic have been focusing on the computing efficiency meanwhile in the assurance of consistent and accurate estimations for the robot map and orientation. Besides, there has been many research in topics such as nonlinearity, data association and landmarks characterization, that are vital to reach a practical and robust SLAM implementation. This research project presents a methodology based on Extended Kalman Filter in an embedded architecture in robots from the Robotics and Automation Research Laboratory of the Electrical Engineering Department. Through the applications, it is intended to accomplish autonomous navigation in internal environments, comparing its performance to the simulated trials and other correlated works.

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