Robotics and Autonomous Systems (Elsevier)

Special Issue on

Visual Control of Mobile Robots

Aims and scope

Visual control refers to the capability of a robot to visually perceive the environment and use this information for autonomous navigation. This task involves solving multidisciplinary problems related with vision and robotics, for example: motion constraints, vision systems, visual perception, safety, real-time constraints, robustness, stability issues, obstacle avoidance… The problem of the vision-based autonomous navigation is also compounded of the different constraints imposed by the particular features of the platform involved (ground platforms, aerial vehicles, underwater robots, humanoids…). Over the last years, increasing efforts have been made to integrate robotic control and vision. Although there is an important number of works in the area of visual control for manipulation, which is a mature field of research, the use of mobile robots add new challenges in a still open research area. The interest in this subject lies in the many potential robotic applications in industrial as well as in domestic settings that involve visual control of mobile robots (automation industry, material transportation, assistance to disabled people, surveillance, rescue, etc).

Special Issue dates:

Deadline for paper submission was on March 31, 2013
After the review process, the accepted papers were published online on April, 2014

Guest Editors:

Youcef Mezouar (Institut Pascal - IFMA, France)
Gonzalo Lopez-Nicolas (I3A - Universidad de Zaragoza, Spain)

Special Issue Contents:

[1] Gonzalo Lopez-Nicolas, Youcef Mezouar, Visual control of mobile robots, Robotics and Autonomous Systems, Volume 62, Issue 11, November 2014, Pages 1611-1612, doi, bib

[2] Hadi Aliakbarpour, Omar Tahri, Helder Araujo, Visual servoing of mobile robots using non-central catadioptric cameras, Robotics and Autonomous Systems, Volume 62, Issue 11, November 2014, Pages 1613-1622, doi, bib

Abstract: This paper presents novel contributions on image-based control of a mobile robot using a general catadioptric camera model. A catadioptric camera is usually made up by a combination of a conventional camera and a curved mirror resulting in an omnidirectional sensor capable of providing 360° panoramic views of a scene. Modeling such cameras has been the subject of significant research interest in the computer vision community leading to a deeper understanding of the image properties and also to different models for different types of configurations. Visual servoing applications using catadioptric cameras have essentially been using central cameras and the corresponding unified projection model. So far only in a few cases more general models have been used. In this paper we address the problem of visual servoing using the so-called radial model. The radial model can be applied to many camera configurations and in particular to non-central catadioptric systems with mirrors that are symmetric around an axis coinciding with the optical axis. In this case, we show that the radial model can be used with a non-central catadioptric camera to allow effective image-based visual servoing (IBVS) of a mobile robot. Using this model, which is valid for a large set of catadioptric cameras (central or non-central), new visual features are proposed to control the degrees of freedom of a mobile robot moving on a plane. In addition to several simulation results, a set of experiments was carried out on Robot Operating System (ROS)-based platform which validates the applicability, effectiveness and robustness of the proposed method for image-based control of a non-holonomic robot.

[3] Hector M. Becerra, Jean-Bernard Hayet, Carlos Sagues, A single visual-servo controller of mobile robots with super-twisting control, Robotics and Autonomous Systems, Volume 62, Issue 11, November 2014, Pages 1623-1635, doi, bib

Abstract: This paper presents a novel approach for image-based visual servoing, extending the existing works that use the trifocal tensor (TT) as source for image measurements. In the proposed approach, singularities typically encountered in this kind of methods are avoided. A formulation of the TT-based control problem with a virtual target resulting from the vertical translation of the real target allows us to design a single controller, able to regulate the robot pose towards the desired configuration, without local minima. In this context, we introduce a super-twisting control scheme guaranteeing continuous control inputs, while exhibiting strong robustness properties. Our approach is valid for perspective cameras as well as catadioptric systems obeying the central camera model. All these contributions are supported by convincing numerical simulations and experiments under a popular dynamic robot simulator.

[4] Geraldo Silveira, On intensity-based 3-D visual servoing, Robotics and Autonomous Systems, Volume 62, Issue 11, November 2014, Pages 1636-1645, doi, bib 

Abstract: This article investigates the problem of pose-based visual servoing whose equilibrium state is defined via a reference image. Differently from most solutions, this work directly exploits the pixel intensities without any feature extraction or matching. Intensity-based methods provide for higher accuracy and versatility. Another central idea of this work concerns the exploitation of the observability issue associated to monocular systems, which always occurs around the equilibrium. This overall framework allows for developing a family of new 3D visual servoing techniques with varying degrees of computational complexity and of prior knowledge, all in a unified scheme. Three new methods are then presented, and their closed-loop performances are experimentally assessed. As an additional contribution, these results refute the common belief that correct camera calibration and pose recovery are crucial to the accuracy of 3D visual servoing techniques.

[5] Jakob Engel, Jurgen Sturm, Daniel Cremers, Scale-aware navigation of a low-cost quadrocopter with a monocular camera, Robotics and Autonomous Systems, Volume 62, Issue 11, November 2014, Pages 1646-1656, doi, bib

Abstract: We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter’s flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects. 

[6] Duy-Nguyen Ta, Kyel Ok, Frank Dellaert, Vistas and parallel tracking and mapping with Wall–Floor Features: Enabling autonomous flight in man-made environments, Robotics and Autonomous Systems, Volume 62, Issue 11, November 2014, Pages 1657-1667, doi, bib

Abstract: We propose a solution towards the problem of autonomous flight in man-made indoor environments with a micro aerial vehicle (MAV), using a frontal camera, a downward-facing sonar, and odometry inputs. While steering an MAV towards distant features that we call vistas, we build a map of the environment in a parallel tracking and mapping fashion to infer the wall structure and avoid lateral collisions in real-time. Our framework overcomes the limitations of traditional monocular SLAM approaches that are prone to failure when operating in feature-poor environments and when the camera purely rotates. First, we overcome the common dependency on feature-rich environments by detecting Wall–Floor Features (WFFs), a novel type of low-dimensional landmarks that are specifically designed for man-made environments to capture the geometric structure of the scene. We show that WFFs not only reveal the structure of the scene, but can also be tracked reliably. Second, we cope with difficult robot motions and environments by fusing the visual data with odometry measurements in a principled manner. This allows the robot to continue tracking when it purely rotates and when it temporarily navigates across a completely featureless environment. We demonstrate our results on a small commercially available quad-rotor platform flying in a typical feature-poor indoor environment.