Recent research in image sensors has produced cameras with very large fields of view. An area of computer vision research which will benefit from this technology is the computation of camera motion (ego-motion) from a sequence of images. Traditional cameras suffer from the problem that the direction of translation may lie outside of the field of view, making the computation of camera motion sensitive to noise. In this paper, we present a method for the recovery of ego-motion using omnidirectional cameras. Noting the relationship between spherical projection and wide-angle imaging devices, we propose mapping the image velocity vectors to a sphere, using the Jacobian of the transformation between the projection model of the camera and spherical projection. Once the velocity vectors are mapped to a sphere, we show how existing ego-motion algorithms can be applied and present some experimental results. These results demonstrate the ability to compute egomotion with omnidirectional cameras....

Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research.

There have been two thrusts in the development of optical flow algorithms. One has emphasized higher accuracy; the other faster implementation. These two thrusts, however, have been independently pursued, without addressing the accuracy vs. efficiency trade-offs. Although the accuracy-efficiency characteristic is algorithm dependent, an understanding of a general pattern is crucial in evaluating an algorithm as far as real world tasks are concerned, which often pose various performance requirements. This paper addresses many implementation issues that have often been neglected in previous research, including subsampling, temporal filtering of the output stream, algorithms' flexibility and robustness, etc. Their impacts on accuracy and/or efficiency are emphasized. We present a critical survey of different approaches toward the goal of higher performance and present experimental studies on accuracy vs. efficiency trade-offs. The goal of this paper is to bridge the gap between the accuracy and the efficiency-oriented approaches.

Algorithms based on the correlation of image patches can be robust in practice but are computationally intensive due to the computational complexity of their search-based nature. Performing the search over time instead of over space is linear in nature, rather than quadratic, and results in a very efficient algorithm. This, combined with implementations which are highly efficient on standard computing hardware, yields performance of 9 frames per second on a scientific workstation. Although the resulting velocities are quantized with resulting quantization error, they have been shown to be sufficiently accurate for many robotic vision tasks such as time-to-collision and robotic navigation. Thus, this algorithm is highly suitable for realtime robotic vision research. 1 Introduction For robotic vision to be successful and practical in real-world environments, it must be robust, fast, and sufficiently accurate as appropriately defined for a given task. If an algorithm is not robust and o...

Abstract—We aim at developing autonomous microflyers capable of navigating within houses or small indoor environments using vision as the principal source of information. Due to severe weight and energy constraints, inspiration is taken from the fly for the selection of sensors, for signal processing, and for the control strategy. The current 30-g prototype is capable of autonomous steering in a IT IT m textured environment. This paper describes models and algorithms which allow for efficient course stabilization and collision avoidance using optic flow and inertial information. Index Terms—Collision avoidance, indoor flying robot, optic flow (OF), steering control. I.

This paper describes a real-time robot vision system which uses only the divergence of the optical flow field for both steering control and collision detection. The robot has wandered about the lab at 20 cm/s for as long as 26 minutes without collision.

endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Our mobile robot, Spinoza, embodies a sophisticated real-time vision system for control of a mobile robot in a dynamic environment. The complexity of our robot architecture arises from the wide variety of tasks that need to be performed and the resulting chal-lenge of coordinating multiple distributed, concurrent processes on a diverse range of processor architec-tures including Transputers, digital signal processors, and a workstation host. The system handles sens-ing, reasoning, and action components of a robot dis-tributed over these architectures, and responds to un-predictable events in an unknown dynamic environ-ment. Spinoza relies heavily on its capability to per-form real-time vision processing in order to perform task such as mapping, navigation, exploration, track-ing, and simple manipulation. 1

Computing a camera's ego-motion from an image sequence is easier to accomplish when a spherical retina is used, as opposed to a standard retinal plane. On a spherical field of view both the focus of expansion and contraction are visible, whereas for a planar retina that is not necessarily the case.

Some recent work with autonomous robots has focused on using optical flow for "direct" control of speed and rotation in obstacle avoidance and other simple behaviors. This work has been inspired by work with insects showing similar mechanisms. To extend these behaviors, three methods of maze navigation are investigated in a simulated robot modeled after a real one. A motor-based method places biases in the obstacle avoidance control law used previously. A perception-based method uses optical flow to detect possibilities for action (e.g., to turn left or right). Both of these require that the agent have a list of biases in order to navigate. The third method, called the Salience Centroid Method, is based on a theory of the role of the hippocampus in rat navigation. This method trades off the memory of the first two for more advanced perceptual processing and allows the most flexible behavior. 1 Introduction In the last few years, a number of researchers have explored the use of optica...