We consider how to cover and map an initially unknown environment using two (or more) mobile robots. Most mobile robot systems accrue odometry error while moving, and hence need to use external sensors to recalibrate their position on an ongoing basis. Unfortunately, most sensing systems are constrained with respect to the types of environment in which they are suitable. We deal with position c...

This work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is de...

This thesis presents simultaneous localization and mapping algorithm for a small mobile robot. The designed solution enables to determine a mobile robot’s and landmark positions using self-localization and camera based landmark detection. As landmarks, roundel patterns are used that are positioned statically on obstacles or borders of the used arena. One of the key features is the suppression o...

In this paper we address the problem of visual motion estimation (visual odometry) from a single vehicle mounted camera. One of the basic issues of visual odometry is relative scale estimation. We propose a method to compute locally optimal relative scales by minimizing the reprojection error using windowed bundle adjustment. We introduce a minimal parameterization of the bundle adjustment prob...

The paper describes several approaches and experimental results for learning a map of an indoor environment, using a combination of odometry, sonar range sensors, and vision. The aim of the presented research work is consistent real-time map-learning in indoor-environments with low-cost sensors and limited computational resources and without installations in the environment itself. Therefore, w...

Although odometry is nonlinear, it yields sufficiently to linearized analysis to produce a closed-form transition matrix and a symbolic general solution for both deterministic and stochastic error propagation. Accordingly, error propagation in vehicle odometry can be understood at a level of theoretical rigor equivalent to the well-known Schuler dynamics of inertial navigation. While response t...

We present an approach to predicting velocity and direction changes from visual information (”visual odometry”) using an end-to-end, deep learning-based architecture. The architecture uses a single type of computational module and learning rule to extract visual motion, depth, and finally odometry information from the raw data. Representations of depth and motion are extracted by detecting sync...

Humanoid robot navigation in domestic environments remains a challenging task. In this paper, we present an approach for navigating such environments for the humanoid robot Nao. We assume that a map of the environment is given and focus on the localization task. The approach is based on the use only of odometry and a single camera. The camera is used to correct for the drift of odometry estimat...

This paper addresses the problem of the odometry error estimation during the robot navigation. The robot is equipped with an external sensor (like laser range finder). Concerning the systematic error an augmented Kalman Filter is introduced. This filter estimates a vector state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. I...

In this paper, fast techniques are proposed to achieve real time and robust monocular visual odometry. We apply an iterative 5point method to estimate instantaneous camera motion parameters in the context of a RANSAC algorithm to cope with outliers efficiently. In our method, landmarks are localized in space using a probabilistic triangulation method utilized to enhance the estimation of the la...