Pose estimation in an indoor environment is of utmost importance for robotic applications. With the absence of a ubiquitous GPS alternative, vision based methods become the go-to approach. This paper introduces a light-weight method for orientation and position estimation from a single frame based on typical indoor architectural features. This method can be used in an autonomous micro aerial vehicle to navigate in real-time with a limited on board processor.

This paper presents a vision-based indoor positioning system of a small robotic vehicle utilizing knowledge of the building floorplan. Using images taken by a monocular camera rigidly mounted onto the deck of the vehicle, the localization system obtains initial geometry of the environment and camera motion by running Structure from Motion. The localization system resolves the scale ambiguity present in the data by associating planar structures in the 3D point cloud with walls of the building. In order to extract the planes, we developed a Scale Invariant Planar RANSAC (SIPR) algorithm which handles situations of scale ambiguity in the point cloud data. Our Wall Plane Fusion algorithm forms correspondences between walls and computed planes, and the best such correspondence is used as an external constraint to the Bundle Adjustment algorithm which is run on the Structure from Motion data. A necessary condition for providing a global positioning solution is that one wall be in view. This paper provides results in both simulated and real-world scenarios.

Bundle Adjustment is the current state of the art method for solving the simultaneous localization and mapping problem. This problem is important for the localization of robots, and most acute for flying robots that cannot rely on ground odometry. The solution requires additional information to resolve scale, and most implementations either assume that relatively accurate pose information exists, or utilize GPS and IMU sensors. This paper presents an alternative approach that incorporates Digital Terrain Model constraints into the Bundle Adjustment algorithm, enabling the correct resolution of scale even in the absence of additional information. It is shown, in multiple test scenarios, that this algorithm provides estimations that do not diverge with time and that exceed, in accuracy, the resolution of the underlying sampled terrain.

This paper addresses the problem of distributed cooperative navigation. A new graph-based method is developed for on-demand calculation of the required correlation terms, considering a general multi-robot measurement model. These correlation terms are necessary for the consistent EKF-based data fusion when several statistically-dependent sources of information are used. The measurement model relates between the navigation information transmitted by any number of robots and the actual readings taken by the available onboard sensors. The transmitted information is not necessarily of the current time instant, but may actually belong to some time instant from the past. Experiment results and a theoretical example of the developed method are presented considering a three-view measurement, formulated upon receiving three images of the same scene, captured by different robots at different a priori unknown time instances.

In this paper we present a novel method for visual scanning and target tracking by means of independent pan-tilt cameras which mimic the chameleon visual system. We present a systematic and optimization-based approach to the problem, from the high-level to the low-level control. In particular, in the first part we develop a new algorithm for scanning the sphere using multiple cameras. The algorithm combines information about the environment and a model of target movement, to perform optimal scanning by means of stochastic dynamic programming. In the second part we develop a model-based control strategy for target tracking. A switching optimal control strategy based on smooth pursuit and saccades is designed by means of explicit model predictive control (MPC) theory. We simulated and experimentally validated our theory on a robotic chameleon head composed of two independent pan-tilt cameras. The resulting scanning pattern and target tracking has a remarkable resemblance to the one seen in nature by chameleons

A novel algorithm for pose and motion estimation using corresponding features and a Digital Terrain Map is proposed. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables the elimination of the ambiguity present in vision-based algorithms for motion recovery. As a consequence, the absolute position and orientation of a camera can be recovered with respect to the external reference frame. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. Explicit reconstruction of the 3D world is not required. When considering a number of feature points, the resulting constraints can be solved using nonlinear optimization in terms of position, orientation, and motion. Such a procedure requires an initial guess of these parameters, which can be obtained from dead-reckoning or any other source. The feasibility of the algorithm is established through extensive experimentation. Performance is compared with a state-of-the-art alternative algorithm, which intermediately reconstructs the 3D structure and then registers it to the DTM. A clear advantage for the novel algorithm is demonstrated in variety of scenarios.

A novel algorithm for pose estimation using feature correspondences and Digital Terrain Map (DTM) is proposed. A constraint is formulated by using corresponding features from two consecutive frames together with the information provided by the elevation map. The proposed constraint is nonlinear and is solved by using a simple numerical method. The proposed approach does not require an intermediate explicit reconstruction of the 3D world. The feasibility of the algorithm is studied using both synthetic data of a virtual terrain and experimental data obtained from a terrain model and a robotic camera.

Fixation is shown to be a visual routine which reduces dead-reckoning errors accumulated by an autonomous guided vehicle (AGV). By fixating on a landmark as the vehicle moves one can improve the navigation accuracy even if the scene coordinates of the landmark are unknown. In contrast with previous methods which assume that the coordinates of the landmark are known, our method enables any point of the observed scene to be selected as a landmark, and not just pre-measured points. Moreover, in contrast with other methods, in fixation only one point needs to be tracked. This disposes of the need to be able to identify which of the landmarks is currently being tracked, through a matching algorithm or by other means. Thus the incorporation of fixation into the navigation process does not involve long computation times and may be done while the vehicle is continuously moving. In addition to the basic method, we suggest an "emergency procedure" for obtaining absolute position once the vehicle gets lost. We support our findings with both experimental and simulation results.

This paper deals with the problem of determining the position and orientation of an autonomous guided vehicle (AGV) by fusing odometry with the information provided by a vision system. The main idea is to exploit the ability of pointing a camera in different directions, to fixate on a point of the environment while the AGV is moving. By fixating on a landmark, one can improve the navigation accuracy even if the scene coordinates of the landmark are unknown. This is a major improvement over previous methods which assume that the coordinates of the landmark are known, since any point of the observed scene can be selected as a landmark, and not just pre-measured points. This work argues that fixation is basically a simpler procedure than previously mentioned methods. The simplification comes from the fact that only one point needs to be tracked as opposed to multiple points in other methods. This disposes of the need to be able to identify which of the landmarks is currently being tracked, through a matching algorithm or by other means. We support our findings with both experimental and simulation results.

Foveated vision and two-mode tracking, as inspired by the human oculomotor system, are often used in active vision system. The purpose of this paper is to provide answers to the following basic questions which arise from implementations. First, is it beneficial to have foveated vision and what is the optimal size of the foveal window? Second, is there a need for two control mechanisms (smooth pursuit and saccade) for improved performance and how can one efficiently switch between them? In order to do so, a setup is proposed in which these strategies can be evaluated in a systematic manner. It is shown that the fovea appears as a compromise between the tightness of the tracking specifications and computational constraints. Introducing a model for the later and postulating some a priori knowledge of the target behavior, it is possible to compute the size of the fovea in an optimal way. As a by-product, "smooth-pursuit" can be defined in a natural way, and the use of a two-mode tracking scheme is justified. The second mode, i.e. "saccadic control", aims at re-centering the target on the fovea so that the smooth pursuit controller can continue to operate. It is shown that a control strategy can indeed be defined so that this objective can be met under appropriate operating conditions.