Active vision for reliable ranging: Cooperating focus, stereo, and vergence

Does vision extract absolute distance from vergence?

Since Kepler (1604) and Descartes (1637), 'vergence' (the angular rotation of the eyes) has been thought of as one of our most important absolute distance cues. But vergence has never been tested as an absolute distance cue divorced from obvious confounding cues such as binocular disparity. In this article, we control for these confounding cues for the first time by gradually manipulating vergence and find that observers fail to accurately judge distance from vergence. We consider several different interpretations of these results and argue that the most principled response to these results is to question the general effectiveness of vergence as an absolute distance cue. Given that other absolute distance cues (such as motion parallax and vertical disparities) are limited in application, this poses a real challenge to our contemporary understanding of visual scale.

Long-Range Binocular Vision Target Geolocation Using Handheld Electronic Devices in Outdoor Environment

Binocular vision is a passive method of simulating the human visual principle to perceive the distance to a target. Traditional binocular vision applied to target localization is usually suitable for short-range area and indoor environment. This paper presents a novel vision-based geolocation method for long-range targets in outdoor environment, using handheld electronic devices such as smart phones and tablets. This method solves the problems in long-range localization and determining geographic coordinates of the targets in outdoor environment. It is noted that these sensors necessary for binocular vision geolocation such as the camera, GPS, and inertial measurement unit (IMU), are intergrated in these handheld electronic devices. This method, employing binocular localization model and coordinate transformations, is provided for these handheld electronic devices to obtain the GPS coordinates of the targets. Finally, two types of handheld electronic devices are used to conduct the experiments for targets in long range up to 500m. The experimental results show that this method yields the target geolocation accuracy along horizontal direction with nearly 20m, achieving comparable or even better performance than monocular vision methods.

Reliable confirmation of an object identity by a mobile robot: A mixed appearance/localization-driven motion approach

This paper investigates the problem of confirming the identity of a candidate object (expected to be a target based on some crude visual clues) with a mobile robot equipped with visual sensing capabilities. We present a method whose main novelty is to mix localization of the robot relative to the candidate object and to confirm that it is the sought target. This twofold approach drastically reduces false positives. Identity confirmation with this twofold goal is modeled as a Partially-Observable Markov Decision Process, where the states are the cells of the space decomposition. It is solved using Stochastic Dynamic Programming with imperfect state information. A robotic system using this method has been implemented and tests have been carried out both in simulation and with a real robot. The experiments empirically validate the use of various metrics, and demonstrate their ability to perform well in different settings.

Dynamic Sensor Planning and Control for Optimally Tracking Targets

In this paper, we present an approach to the problem of actively controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configuration is particularly important in the context of teams equipped with vision sensors, since most estimation schemes of interest will involve some form of triangulation.

We provide a theoretical framework for tackling the sensor planning problem, and a practical computational strategy inspired by work on particle filtering for implementing the approach. We then extend our framework by showing how modeled system dynamics and configuration space obstacles can be handled. These ideas have been applied to a target tracking task, and demonstrated both in simulation and with actual robot platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.

Active vision in robotic systems: A survey of recent developments

In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.

Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks

Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.

RECIPROCAL-WEDGE TRANSFORM IN ACTIVE STEREO

The Reciprocal-Wedge Transform (RWT) facilitates space-variant image representation. In this paper a V-plane projection method is presented as a model for imaging using the RWT. It is then shown that space-variant sensing with this new RWT imaging model is suitable for fixation control in active stereo that exhibits vergence and versional eye movements and scanpath behaviors. A computational interpretation of stereo fusion in relation to disparity limit in space-variant imagery leads to the development of a computational model for binocular fixation. The vergence-version movement sequence is implemented as an effective fixation mechanism using the RWT imaging. A fixation system is presented to show the various modules of camera control, vergence and version.