This paper presents an algorithm to correct the odometry error of an autonomous mobile robot only by using a painted grid on the floor. The supposed robot position is calculated by odometry and is matched with the grid lines, whose existence is known to the robot. A new “position probability function” was developed and used by the correction algorithm. The correction of the odometry error is al...

Wheel odometry is a common method for mobile robot relative localisation. However, this method is known to suffer from systematic errors. In this paper a comprehensive systematic odometry error model for a synchronous drive robot is proposed. The model addresses the systematic error for both rotational and translational motions. Results on real data show a real potential to produce a significan...

This work presents an improved visual odometry using omnidirectional images. The main purpose is to generate a reliable prior input which enhances the SLAM (Simultaneous Localization and Mapping) estimation tasks within the framework of navigation in mobile robotics, in detriment of the internal odometry data. Generally, standard SLAM approaches extensively use data such as the main prior input...

Odometry is widely used to localize wheeled and tracked vehicles because of its simplicity and continuity. Odometric calculations integrate the wheel or track’s rotation speed. The accuracy of position thus calculated, is affected by slippage between the ground and the wheel or track. When traveling on a loose slope, the localization accuracy of the odometry decreases remarkably due to slippage...

Odometry using incremental wheel encoder sensors provides the relative position of mobile robots. This relative position is fundamental information for pose estimation by various sensors for EKF Localization, Monte Carlo Localization etc. Odometry is also used as unique information for localization of environmental conditions when absolute measurement systems are not...

Pose estimation for mobile robots depends basically on accurate odometry information. Odometry from the wheel’s encoder is widely used for simple and inexpensive implementation. As the travel distance increases, odometry suffers from kinematic modeling errors regarding the wheels. Therefore, in order to improve the odometry accuracy, it is necessary that sy...

This paper presents a very simple, yet very effective method for combining measurements from a gyro with measurements from wheel encoders (odometry). Sensor-fusion of this kind has been done before, usually by means of a statistical model that describes the behavior of the gyro and the behavior of the odometry component. However, because these systems are based on models, they cannot anticipate...

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situat...

Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recent approaches to single view depth estimation explore the possibility of learning without full supervision via minimizing photometric error. In this paper, we explore the use of stereo sequences for learning depth and ...