In this paper, we address the problem of controlling a mobile stereo camera under image quantization noise. Assuming that a pair of images of a set of targets is available, the camera moves through a sequence of Next-Best-Views (NBVs), i.e., a sequence of views that minimize the trace of the targets' cumulative state covariance, constructed using a realistic model of the stereo rig that captures image quantization noise and a Kalman Filter (KF) that fuses the observation history with new information. The proposed algorithm decomposes control into two stages: first the NBV is computed in the camera relative coordinates, and then the camera moves to realize this view in the fixed global coordinate frame. This decomposition allows the camera to drive to a new pose that effectively realizes the NBV in camera coordinates while satisfying Field-of-View constraints in global coordinates, a task that is particularly challenging using complex sensing models. We provide simulations and real experiments that illustrate the ability of the proposed mobile camera system to accurately localize sets of targets. We also propose a novel data-driven technique to characterize unmodeled uncertainty, such as calibration errors, at the pixel level and show that this method ensures stability of the KF.
 This paper presents a decentralized motion planning algorithm for the distributed sensing of a noisy dynamical process by multiple cooperating mobile sensor agents. This problem is motivated by localization and tracking tasks of dynamic targets. Our gradient-descent method is based on a cost function that measures the overall quality of sensing. We also investigate the role of imperfect communication between sensor agents in this framework, and examine the trade-offs in performance between sensing and communication. Simulations illustrate the basic characteristics of the algorithms This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization. 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 computati... In this paper, we study the problem of optimal trajectory generation for a team of heterogeneous robots moving in a plane and tracking a moving target by processing relative observations, i.e., distance and or bearing. Contrary to previous approaches, we explicitly consider limits on the robots' speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single tracking sensor and seek the next sensing location in order to minimize the uncertainty about the target's position. We show that although the corresponding optimization problem involves a nonconvex objective function and a nonconvex constraint, its global optimal solution can be determined analytically. We then extend the approach to the case of multiple sensors and propose an iterative algorithm, i.e., the Gauss-Seidel relaxation (GSR), to determine the next best sensing location for each sensor. Extensive simulation results demonstrate that the GSR algorithm, whose computational complexity is linear in the number of sensors, achieves higher tracking accuracy than gradient descent methods and has performance that is indistinguishable from that of a grid-based exhaustive search, whose cost is exponential in the number of sensors. Finally, through experiments, we demonstrate that the proposed GSR algorithm is robust and applicable to real systems. In this paper, we present a new approach to the problem of simultaneously localizing a group of mobile robots capable of sensing one another. Each of the robots collects sensor data regarding its own motion and shares this information with the rest of the team during the update cycles. A single estimator, in the form of a Kalman filter, processes the available positioning information from all the members of the team and produces a pose estimate for every one of them. The equations for this centralized estimator can be written in a decentralized form, therefore allowing this single Kalman filter to be decomposed into a number of smaller communicating filters. Each of these filters processes the sensor data collected by its host robot. Exchange of information between the individual filters is necessary only when two robots detect each other and measure their relative pose. The resulting decentralized estimation schema, which we call collective localization, constitutes a unique means for fusing measurements collected from a variety of sensors with minimal communication and processing requirements. The distributed localization algorithm is applied to a group of three robots and the improvement in localization accuracy is presented. Finally, a comparison to the equivalent decentralized information filter is provided. Abstract We present an approach for directing next-step movements of robot teams engaged in mapping objects in their environment: Move Value Estimation for Robot Teams (MVERT). Resulting robot paths tend to optimize vantage points for all robots on the team by maximizing information gain. At each step, each robot selects a movement to maximize the utility (in this case, reduction in uncertainty) of its next observation. Trajectories are not guaranteed to be optimal, but team behavior serves to maximize the team's knowledge since each robot considers the observational contributions of team mates. MVERT is evaluated in simulation by measuring the resulting uncertainty about target locations compared to that obtained by robots acting without regard to team mate locations and to that of global optimization over all robots for each single step. Additionally, MVERT is demonstrated on physical teams of robots. The qualitative behavior of the team is appropriate and close to the single-step optimal set of trajectories. This paper studies the active target-tracking problem for a team of unmanned aerial vehicles equipped with 3-D range-finding sensors. We propose a gradient-based control strategy that encompasses the three major optimum experimental design criteria, and we use the Kalman filter for estimating the target's position both in a cooperative and in a noncooperative scenario. Our control strategy is active because it moves the vehicles along paths that minimize the uncertainty about the location of the target. In the case that the position of the vehicles is not perfectly known, we introduce a new and more challenging problem, termed active cooperative localization and multitarget tracking (ACLMT). In this problem, the aerial vehicles must reconfigure themselves in the 3-D space in order to maximize both the accuracy of their own position estimate and that of multiple moving targets. For ACLMT, we derive analytical lower and upper bounds on the targets' and vehicles' position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of the sensors' accuracy and the targets' dynamics on the performance of our coordination strategy. Extensive simulation experiments illustrate the proposed theoretical results.