Hector Rotstein

Partial Aircraft State Estimation from Visual Motion Using the Subspace Constraints Approach

The estimation of an aircraftmotionfrom the optical x8f ow observed by a downward-lookingbody-x8e xed camera is discussed. The estimation is based on the so-called subspace constraint, which arises when points stationary on the environment are tracked on the image plane. The constraint can be combined with the aircraft dynamics, giving rise to a nonlinear estimation problem. The problem was solved using an implicit extended Kalman x8e lter. The suggested algorithmwas implemented in a simulation,which verix8e ed that the angle of attack, the angle of sideslip, and the angular body pitch, yaw, and roll rates could be estimated. The estimation was shown to be unbiased with a Monte Carlo method. Furthermore, the standard deviations of the estimation errors converged to reasonable values after a relatively small time interval. An important feature of the method is that good performance was achieved even when tracking a relatively small number of feature points, implyingmodest real-time computational needs. The estimated signals could be used either for navigationor control.

Real-Time Vision-Aided Localization and Navigation Based on Three-View Geometry

A new method for vision-aided navigation based on three-view geometry is presented. The main goal of the proposed method is to provide position estimation in GPS-denied environments for vehicles equipped with a standard inertial navigation system (INS) and a single camera only, without using any a priori information. Images taken along the trajectory are stored and associated with partial navigation data. By using sets of three overlapping images and the concomitant navigation data, constraints relating the motion between the time instances of the three images are developed. These constraints include, in addition to the well-known epipolar constraints, a new constraint related to the three-view geometry of a general scene. The scale ambiguity, inherent to pure computer vision-based motion estimation techniques, is resolved by utilizing the navigation data attached to each image. The developed constraints are fused with an INS using an implicit extended Kalman filter. The new method reduces position errors in all axes to the levels present while the first two images were captured. Navigation errors in other parameters are also reduced, including velocity errors in all axes. Reduced computational resources are required compared with bundle adjustment and simultaneous localization and mapping (SLAM). The proposed method was experimentally validated using real navigation and imagery data. A statistical study based on simulated navigation and synthetic images is presented as well.

Pose and motion recovery from feature correspondences and a digital terrain map

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

Fusion of fixation and odometry for vehicle navigation

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.

Graph-based distributed cooperative navigation for a general multi-robot measurement model

Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all of the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, with a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly developed method is computationally efficient.

An exact solution to continuous-time mixed H/sub 2//H/sub /spl infin// control problems

Multiobjective control problems have been the object of much attention in the past few years, since they allow for handling multiple, perhaps conflicting, performance specifications and model uncertainty. One of the earliest multiobjective problems is the mixed H/sub 2//H/sub /spl infin// control problem, which can be motivated as a nominal LQG optimal control problem subject to robust stability constraints. This problem has proven to be surprisingly difficult to solve, and at this time no closed-form solutions are available. Moreover, it has been shown that except in some trivial cases, the optimal controller is infinite-dimensional. In this paper, we propose a solution to general continuous-time mixed H/sub 2//H/sub /spl infin// problems, based upon constructing a family of approximating problems, obtained by solving an equivalent discrete-time problem. Each of these approximations can be solved efficiently, and the resulting controllers converge strongly in the H/sub 2/ topology to the optimal solution.

Navigation Aiding Based on Coupled Online Mosaicking and Camera Scanning

This paper presents a new method for vision-aided navigation of airborne platforms. The method is based on online mosaicking using images acquired by an onboard gimballed camera, which scans ground regions in the vicinity of the flight trajectory. The coupling of the scanning and mosaicking processes improves image-based motion estimation when operating in challenging scenarios such as narrow field-of-view cameras observing low-texture scenes. These improved motion estimations are fused with an inertial navigation system. The mosaic used for navigation is constructed in two levels. A small mosaic based on recently captured images is computed in real-time, and a larger mosaic including all the images is computed in a background process. The low-level mosaic is used for immediate motion estimation, while the higher-level mosaic is used for global navigation. The correlation terms between the navigation system and the mosaic construction process are not maintained in the proposed approach. The advantage of this architecture is the low computational load required for navigation aiding. However, the accuracy of the proposed method could be compromised compared with bearing-only simultaneous localization and mapping. The new method was examined using statistical simulation runs and experiments based on Google Earth imagery, showing its superior performance compared with traditional methods for two-view navigation aiding.

Distributed vision-aided cooperative localization and navigation based on three-view geometry

This paper presents a new method for distributed vision-aided cooperative localization and navigation for multiple autonomous platforms based on constraints stemming from the three-view geometry of a general scene. Each platform is assumed to be equipped with a standard inertial navigation system and an on-board, possibly gimbaled, camera. The platforms are also assumed to be capable of intercommunicating. No other sensors, or any a priori information is required. In contrast to the traditional approach for cooperative localization that is based on relative pose measurements, the proposed method formulates a measurement whenever the same scene is observed by different platforms. Each such measurement is constituted upon three images, which are not necessarily captured at the same time. The captured images, attached with some navigation parameters, are stored in repositories by each, or some, of the platforms in the group. A graph-based approach is applied for calculating the correlation terms between the navigation parameters associated to images participating in the same measurement. The proposed method is examined using a statistical simulation in a leader-follower scenario, and is demonstrated in an experiment that involved two vehicles in a holding pattern scenario.

Two-mode control: an oculomotor-based approach to tracking systems

The paper aims to use the knowledge about how the visual system organizes the components of oculomotor system to propose a new tracking paradigm. The tracking system is assumed to be described by linear time-invariant discrete-time state-space equations. The approach described, motivated by the behavior of the visual system, is to switch off the smooth controller whenever a violation occurs and design a time-optimal control action, i.e., a saccade, to drive the control system so that the constraint is satisfied after the shortest possible time interval. After that, the smooth controller is switched back into the loop. The way this switching is performed is critical for obtaining good behavior. A method is proposed which is based on a careful definition of the target set for the saccade. The tracking system proposed in this paper is closely related to recent results in linear optimal and robust control theory.

Estimating camera pose using Bundle Adjustment and Digital Terrain Model constraints

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.