Abstract — In the Autonomous City Explorer (ACE) project a mobile robot is developed, which is capable of finding its way to a given destination in an unknown urban environment. An exemplary mission is to find the way from our institute to the Marienplatz, a public place in the center of Munich, without any prior knowledge or GPS information. Inspired by the behavior of humans in unknown environments, ACE must find its way by asking pedestrians. The route is about 1.5 kilometers far and includes heavily traveled roads and crowded public places. In order to navigate safely in an unknown urban environment, some challenges arise for the vision system. Robust human detection, tracking and the estimation of human body poses is essential for natural interaction with pedestrians. Furthermore, the robot needs to be able to detect sidewalk and crossroads. A visual odometry system is used to support the conventional navigation. Outdoor experiments were conducted twice successfully. After about 5 hours and interacting with 25 and 38 persons respectively, ACE arrived the Marienplatz. This paper describes both, an architecture of the vision system used for ACE and the algorithms used to deal with the described challenges. I.

Abstract — This paper is concerned with the problem of estimating the motion of a single camera from a sequence of images, with an application scenario of vehicle egomotion estimation. Egomotion estimation has been an active area of research for many years and various solutions to the problem have been proposed. Many methods rely on optical flow or local image features to establish the spatial relationship between two images. A new method of egomotion estimation is presented which makes use of the Fourier-Mellin Transform for registering images in a video sequence, from which the rotation and translation of the camera motion can be estimated. The Fourier-Mellin Transform provides an accurate and efficient way of computing the camera motion parameters. It is a global method that takes the contributions from all pixels into account. The performance of the proposed approach is compared to two variants of optical flow methods and results are presented for a real-world video sequence taken from a moving vehicle. I.

Abstract The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth becomes specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a testbed for other algorithms such as segmentation This work has been partly supported by the Spanish Government

Abstract- Robot photographers have appeared in a variety of novelty settings over the past few years and typically have exploited rudimentary image-content-based approaches to identifying potential photographic subjects. These approaches are primarily limited to human subjects and further progress along content-based lines is hamstrung by slow progress on the general computer vision problem. In this paper, we present a mobile robot system which solves the group-picture-framing problem without requiring content-based methods. The system finds photographic subjects based on measurements of motion parallax obtained via optical flow during robot movements. Our method requires only sufficient contrast to permit reasonably accurate sparse optical flow field estimation and is completely independent of any content-based image heuristics. The result is a working

This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We deal with the extended motion of the imaging system through an environment that we assume to have some degree of statistical regularity. For example, in autonomous ground vehicles the structure of the environment around the vehicle is far from arbitrary, and the depth at each pixel is often approximately constant. The subspace constraints hold not only for perspective cameras, but in fact for a very general class of imaging systems, including catadioptric and multiple-view systems. Using minimal assumptions about the imaging system, we learn a probabilistic subspace constraint that captures the statistical regularity of the scene geometry relative to an imaging system. We propose an extension to probabilistic PCA (Tipping and Bishop, 1999) as a way to robustly learn this subspace from recorded imagery, and demonstrate its use in conjunction with a sparse optical flow algorithm. To deal with the sparseness of the input flow, we use a generative model to estimate the subspace using only the observed flow measurements. Additionally, to identify and cope with image regions that violate subspace constraints, such as moving objects, objects that violate the depth regularity, or gross flow estimation errors, we employ a per-pixel Gaussian mixture outlier process. We demonstrate results of finding the optical flow subspaces and employing them to estimate dense flow and to recover camera motion for a variety of imaging systems in several different environments. 1.

Abstract — We present and examine a technique for estimating the ego-motion of a mobile robot using memory-based learning and a monocular camera. Unlike other approaches that rely heavily on camera calibration and geometry to compute trajectory, our method learns a mapping from sparse optical flow to platform velocity and turn rate. We also demonstrate an efficient method of computing high-quality sparse optical flow, and techniques for using this sparse optical flow as input to a supervised learning method. We employ a voting scheme of many learners that use subsets of the sparse optical flow to cope with variable dimensionality and reduce the dimensionality of each learner. Finally, we perform experiments in which we examine the learned mapping for visual odometry, investigate the effects of varying the reduced dimensionality of the sparse optical flow state, and quantify the accuracy of two variations of our learner scheme. Our results indicate that our learning scheme estimates monocular visual odometry mainly from points on the ground plane, and reflect to a degree the minimum dimensionality imposed by the problem. In addition, we show that while this memory-based learning method cannot yet estimate ego-motion as accurately as recent geometric methods, it is possible to learn, with no explicit model of camera calibration or scene structure, complicated mappings that take advantage of properties of the camera and the environment. I.

In the project Autonomous City Explorer (ACE) a mobile robot should autonomously, efficiently and safely navigate in unstructured urban environments. From the biological aspect, the robot should not only plan its visual attention to acquire essential information about the unknown real world but also estimate the ego motion for the navigation based on

Abstract — The goal of the Autonomous City Explorer (ACE) is to navigate autonomously, efficiently and safely in an unpredictable and unstructured urban environment. To achieve this aim, an accurate localization is one of the preconditions. Due to the characteristics of our navigation environment, an elaborated visual odometry system is proposed to estimate the current position and orientation of the ACE platform. The existing algorithms of optical flow computation are experimentally evaluated and compared. The method based on pyramidal Lucas-Kanade algorithm with high-speed performance is selected. Based on the optical flow in 2D images, the camera ego-motion is estimated using image Jacobian matrix and least squares method. The kinematic model is set up to map the camera ego-motion to the robot motion. To eliminate systematic errors, a novel system calibration approach is proposed. Finally the odometry system is evaluated in experiments. I.

This thesis has been performed at the Control Laboratory of the University of Twente. The mechanical design of a mobile robot platform and the design and practical implementation of a purely vision based navigation algorithm under real-time constraints are presented in this report. General requirements for this project are to use vision for navigation and to use cheap, market available hardware. In the scope of this project the hardware design has a lower priority then the theoretical study and development of the vision based control algorithm. The constructed mobile platform is equipped with a single web camera and 1.2Ghz Mini ITX board is used to process the visual data. To steer the motors of the robot the RT-AVR microcontroller board is used. The robot is equipped with a wireless card and interaction with the robot is performed via Internet using the implemented graphical user interface. Vision based estimations are performed at rate of 15 frames per second. The implemented purely vision based navigation algorithm relies on the estimation of the frontal optical flow. The control strategy behind the algorithm is an optical flow balance strategy. In order to achieve a desired behavior the balance strategy has been extended with rotation estimations

Abstract — An optical flow odometry method for mobile robots using a single downward-looking camera is presented. The method is robust to the robot’s own moving shadow and other sources of error. Robustness derives from two techniques: prevention of feature selection on or near shadow edges and elimination of outliers based on inconsistent motion. In tests where the robot’s shadow dominated the image, prevention of feature selection near shadow edges allowed accurate velocity estimation when outlier rejection alone failed. Performance was evaluated on two robot platforms and on multiple terrain types at speeds up to 2 m/s. I.