Vision-based navigation of unmanned aerial vehicles

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled, stored and organized as a set of ordered key images (visual path) which provides a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with an X4-flyer equipped with a fisheye camera.     

Reduction of the uncertainty in feature tracking

It is difficult to establish feature correspondences between distant viewpoints for panoramic images. For reliable navigation and development a human-like capability of interaction with the surrounding environment, we need a method of reduction of the uncertainty in feature tracking. To obtain a method of reduction of the uncertainty in feature tracking, we propose to use an algorithm for the problem of the longest common subsequence for a set of circular strings. We consider an explicit reduction from the problem of the longest common subsequence for a set of circular strings to the satisfiability problem. This reduction allows to obtain an efficient algorithm for finding the longest common subsequence for a set of circular strings. We present a general scheme of the method of reduction of the uncertainty in feature tracking. We considered the visual homing task to demonstrate the capabilities of our approach to solve the problem of reduction of the uncertainty in feature tracking. We present experimental results for the method of reduction of the uncertainty in feature tracking and novel robot visual homing methods.     

Computational capabilities of local-feedback recurrent networksacting as finite-state machines

In this paper we explore the expressive power of recurrent networks with local feedback connections for symbolic data streams. We rely on the analysis of the maximal set of strings that can be shattered by the concept class associated to these networks (i.e. strings that can be arbitrarily classified as positive or negative), and find that their expressive power is inherently limited, since there are sets of strings that cannot be shattered, regardless of the number of hidden units. Although the analysis holds for networks with hard threshold units, we claim that the incremental computational capabilities gained when using sigmoidal units are severely paid in terms of robustness of the corresponding representation.     

Symbolic control of linear systems based on symbolic subsystems

This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem into controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification.     

Qualitative spatial representations from task-oriented perception and exploratory behaviors

We describe motor and perceptual behaviors that have proven useful for indoor navigation of an autonomous mobile robot. These behaviors take advantage of the large amount of structure that characterizes many indoor, office-like environments. Based on pre-existing structural landmarks, a mobile robot has the ability to explore, map, and navigate one among several office buildings sharing similar structural features, while coping with slow environment variations and local dynamics. The mobile robot develops and maintains an internal spatial representation of the environment in terms of a topological and qualitative map. The types of structural features suitable as navigation landmarks largely depend upon the available sensors. Adequate navigation performance is achieved by subdividing perception and navigation into a number of behaviors layered upon a multi-threaded real-time control architecture.     

Attentional Landmarks and Active Gaze Control for Visual SLAM

This paper is centered around landmark detection, tracking, and matching for visual simultaneous localization and mapping using a monocular vision system with active gaze control. We present a system that specializes in creating and maintaining a sparse set of landmarks based on a biologically motivated feature-selection strategy. A visual attention system detects salient features that are highly discriminative and ideal candidates for visual landmarks that are easy to redetect. Features are tracked over several frames to determine stable landmarks and to estimate their 3-D position in the environment. Matching of current landmarks to database entries enables loop closing. Active gaze control allows us to overcome some of the limitations of using a monocular vision system with a relatively small field of view. It supports 1) the tracking of landmarks that enable a better pose estimation, 2) the exploration of regions without landmarks to obtain a better distribution of landmarks in the environment, and 3) the active redetection of landmarks to enable loop closing in situations in which a fixed camera fails to close the loop. Several real-world experiments show that accurate pose estimation is obtained with the presented system and that active camera control outperforms the passive approach.      

A mapping and localization framework for scalable appearance-based navigation

This paper presents a vision framework which enables feature-oriented appearance-based navigation in large outdoor environments containing other moving objects. The framework is based on a hybrid topological–geometrical environment representation, constructed from a learning sequence acquired during a robot motion under human control. At the higher topological layer, the representation contains a graph of key-images such that incident nodes share many natural landmarks. The lower geometrical layer enables to predict the projections of the mapped landmarks onto the current image, in order to be able to start (or resume) their tracking on the fly. The desired navigation functionality is achieved without requiring global geometrical consistency of the underlying environment representation. The framework has been experimentally validated in demanding and cluttered outdoor environments, under different imaging conditions. The experiments have been performed on many long sequences acquired from moving cars, as well as in large-scale real-time navigation experiments relying exclusively on a single perspective vision sensor. The obtained results confirm the viability of the proposed hybrid approach and indicate interesting directions for future work.     

Visual Recognition of Workspace Landmarks for Topological Navigation

In this work, robot navigation is approached using visual landmarks. Landmarks are not preselected or otherwise defined a priori; they are extracted automatically during a learning phase. To facilitate this, a saliency map is constructed on the basis of which potential landmarks are highlighted. This is used in conjunction with a model-driven segregation of the workspace to further delineate search areas for landmarks in the environment. For the sake of robustness, no semantic information is attached to the landmarks; they are stored as raw patterns, along with information readily available from the workspace segregation. This subsequently facilitates their accurate recognition during a navigation session, when similar steps are employed to locate landmarks, as in the learning phase. The stored information is used to transform a previously learned landmark pattern, according to the current position of the observer, thus achieving accurate landmark recognition. Results obtained using this approach demonstrate its validity and applicability in indoor workspaces.     

Occlusion-free animation of driving routes for car navigation systems

This paper presents a method for occlusion-free animation of geographical landmarks, and its application to a new type of car navigation system in which driving routes of interest are always visible. This is achieved by animating a nonperspective image where geographical landmarks such as mountain tops and roads are rendered as if they are seen from different viewpoints. The technical contribution of this paper lies in formulating the nonperspective terrain navigation as an inverse problem of continuously deforming a 3D terrain surface from the 2D screen arrangement of its associated geographical landmarks. The present approach provides a perceptually reasonable compromise between the navigation clarity and visual realism where the corresponding nonperspective view is fully augmented by assigning appropriate textures and shading effects to the terrain surface according to its geometry. An eye tracking experiment is conducted to prove that the present approach actually exhibits visually-pleasing navigation frames while users can clearly recognize the shape of the driving route without occlusion, together with the spatial configuration of geographical landmarks in its neighborhood.     

Arbitrary decay rate for two connected strings with joint anti-damping by boundary output feedback

In this paper, we are concerned with the boundary stabilization of two connected strings with middle joint anti-damping for which all eigenvalues of the (control) free system are located on the right complex plane. We first design an explicit state feedback controller to achieve exponential stability for the closed-loop system. Consequently, we design the output feedback by using infinite-dimensional observer. The backstepping approach is adopted in investigation. It is shown that by using one boundary stabilizer only, the output feedback can make the closed-loop system exponentially stable with arbitrary decay rate.     

Integrating planning and control for single-bodied wheeled mobile robots

This paper presents an approach to couple path planning and control for mobile robot navigation in a hybrid control framework. We build upon an existing hybrid control approach called sequential composition, in which a set of feedback control policies are prescribed on well-defined domains contained in the robot’s free space. Each control policy drives the robot to a goal set, which lies in the domain of a subsequent policy. Control policies are deployed into the free state space so that when composed among one another, the overall action of the set of control policies drives the robot to perform a task, such as moving from a start to a goal location or patrolling a perimeter. A planner determines the sequence of control policies to be invoked. When control policies defined in this framework respect the low-level dynamics and kinematics of the system, this formal approach guarantees that high-level tasks are either accomplished by a given set of policies, or verifies that the tasks are not achievable with the given policies.     

Visual Navigation in Natural Environments: From Range and Color Data to a Landmark-Based Model

This paper concerns the exploration of a natural environment by a mobile robot equipped with both a video color camera and a stereo-vision system. We focus on the interest of such a multi-sensory system to deal with the navigation of a robot in an a priori unknown environment, including (1) the incremental construction of a landmark-based model, and the use of these landmarks for (2) the 3-D localization of the mobile robot and for (3) a sensor-based navigation mode.For robot localization, a slow process and a fast one are simultaneously executed during the robot motions. In the modeling process (currently 0.1 Hz), the global landmark-based model is incrementally built and the robot situation can be estimated from discriminant landmarks selected amongst the detected objects in the range data. In the tracking process (currently 4 Hz), selected landmarks are tracked in the visual data; the tracking results are used to simplify the matching between landmarks in the modeling process.Finally, a sensor-based visual navigation mode, based on the same landmark selection and tracking, is also presented; in order to navigate during a long robot motion, different landmarks (targets) can be selected as a sequence of sub-goals that the robot must successively reach.     

Output feedback stabilisation of a tree-shaped network of vibrating strings with non-collocated observation

In this article, the authors consider the simple tree-shaped network of strings with non-collocated observation. Suppose that the network consists of three strings and the displacements of these strings are continuous at the common vertex. The sensors and actuators are required to be set at different vertices. We propose an observer-based output feedback controller to stabilise this network. The exponential stability of the closed-loop network is obtained based on the Riesz basis approach together with estimates of the spectral distribution. Finally, a numerical simulation is given to support these results.     

A new space and time sensor fusion method for mobile robot navigation

To fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 2004 Wiley Periodicals, Inc.     

Effective landmark placement for accurate and reliable mobile robot navigation

Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.     

Learning landmark triples by experimentation

This article describes a method for learning a set of landmarks suitable for place navigation. The approach is novel in that it exploits the ability of a robot to learn through active perception in the task environment, similar to the learning by experimentation technique developed for LEX (Mitchell et al., 1990). The proposed strategy uses heuristics to select and rank candidate triples, then generates test cases to confirm that the best triple is sufficient. The method supports the use of multiple sensors with different computational and energy costs, where a utility function captures the tradeoff between navigational performance ranking and cost.

Over 100 data points were collected on a mobile robot using a laser barcode reader and computer vision to identify landmarks. The results indicated that active perception and experimentation identified triples with better navigational properties. Furthermore, the learning process is proactive: it was shown to prevent the robot from learning a triple which was not visible over the entire navigational space and/or was not sufficient in practice.     

Iconic indexing by 2-d strings

In this paper, we describe a new way of representing a symbolic picture by a two-dimensional string. A picture query can also be specified as a 2-D string. The problem of pictorial information retrieval then becomes a problem of 2-D subsequence matching. We present algorithms for encoding a symbolic picture into its 2-D string representation, reconstructing a picture from its 2-D string representation, and matching a 2-D string with another 2-D string. We also prove the necessary and sufficient conditions to characterize ambiguous pictures for reduced 2-D strings as well as normal 2-D strings. This approach thus allows an efficient and natural way to construct iconic indexes for pictures.     

Synthesis of Obfuscation Policies to Ensure Privacy and Utility

We consider the problem of privacy enforcement for dynamic systems using the technique of obfuscation. Our approach captures the trade-off between privacy and utility, in a formal reactive framework. Specifically, we model a dynamic system as an automaton or labeled transition system with predefined secret behaviors. The system generates event strings for some useful computation (utility). At the same time, it must hide its secret behaviors from any outside observer of its behavior (privacy). We formally capture both privacy and utility specifications within the model of the system. We propose as obfuscation mechanism for privacy enforcement the use of edit functions that suitably alter the output behavior of the system by inserting, deleting, or replacing events in its output strings. The edit function must hide secret behaviors by making them observationally equivalent to non-secret behaviors, while at the same time satisfying the utility requirement on the output strings. We develop algorithmic procedures that synthesize a correct-by-construction edit function satisfying both privacy and utility specifications. The synthesis procedure is based on the solution of a game where the edit function must react to the system moves by suitable output editing. After presenting an explicit algorithm for solving for the winning strategies of the game, we present two complementary symbolic implementations to address scalability of our methodology. The first symbolic implementation uses a direct encoding of the explicit algorithm using binary decision diagrams (BDDs). The second symbolic implementation reframes the synthesis of edit functions as a supervisory control problem and then applies a recently-developed tool for solving supervisory control problems using BDDs. Experimental results comparing the two symbolic implementations are provided.