This paper presents a guidance system for au- tonomous vehicles navigation in semi-structured outdoor envi- ronments. It integrates redundant encoders data and absolute positioning data provided by landmarks and artificial beacons. Natural features are localized using a laser range sensor, and magnetic sensing rulers were developed to detect magnetic markers buried in the ground. In the first fusion stage, data from four wheel encoders and one steering encoder are fused by means of an EKF, providing robust odometric information, namely in face of undesirable effects of wheels slippage. Next, a second fusion stage is processed for integrating odometric and absolute positioning data. Simulation and real experiments using a four-wheels actuated electrical vehicle are presented. I. INTRODUCTION Odometry being essential for autonomous navigation, is not enough due to its relative and integrative nature. So, it is required to complement odometric data with absolute positioning. Due to systematic error problems, some effort has been dedicated in modelling the uncertainty propagation in order to get somehow a reliable odometry model (6). Data fusion of ABS sensors and GPS for outdoor localization, based on an Extended Kalman Filter (EKF) had been pre- sented by Bonnifait, et al. (2).

This paper describes the ISRobotCar, an experimental autonomous electric vehicle that integrates Robot Operating System (ROS) and several sensors such as IBEO laserscanner, Inertial Measurement Unit (IMU), RTK-GPS, vision cameras, and magnet detectors for magnetic guidance. The development of a power steering controller and of a fuzzy path-following controller are particularly addressed. Finally simulations and experimental results of autonomous path-following control are presented.

This paper presents an outdoor guidepath navigation system for autonomous mobile robots (AMR) that use permanent magnetic markers embedded in the ground. The odometric data provided by the wheel encoders is fused with the data from magnetic markers. The extended Kalman filter (EKF) was chosen for the fusion process. The AMR is equipped with a magnetic sensing ruler (MSR) developed at ISR-UC that is able to perform a robust detection of magnetic markers. The detection is based on a 3-D algorithm that includes longitudinal-fitting detection (LFD), and cross-fitting detection (CFD). Both, the LFD and the CFD are based on the least squares fitting (LSF) of the measurement data with the 3-D model of the vertical magnetic field. The experimental results with Robchair (intelligent wheelchair being developed at ISR-UC) primarily show that the detection system is robust, since it is able to detect true magnetic markers, and to eliminate noisy magnetic distortions and false markers. The design, and implementation of the navigation algorithm in the Robchair were carried out, and results are presented.

The video shows an overview of the ISRobotCar, an experimental autonomous electric vehicle that integrates the robot operating system (ROS.org) and several sensors such as an IBEO laser scanner, inertial measurement units (IMU), RTK-GPS, vision cameras, and magnet detectors for magnetic guidance. The main purpose of this vehicle is to serve as a platform for experimental testing of algorithms required for autonomous navigation and cooperative navigation in urban environments. In the video, we present the ISRobotCar moving autonomously in a parking-like area as well as its main hardware and control modules.