Penny Probert

A stereo vision-based aid for the visually impaired

This paper describes a portable vision-based obstacle detection system, intended for use by blind people. The system combines an obstacle detection system designed for AGVs with recalibration of ground position and a Kalman Filter based model of the persons walking movement. The system uses stereo vision. Obstacle detection is achieved through comparison of the disparity seen with that expected from the position of the ground. Recalibration of ground position is made by plane fitting in the ground region. Motion estimation using two visual methods and the use of an inclinometer is described. The results show satisfactory success in all parts of the system.

Robotic sensing for the partially sighted

Partial sightedness is a sensory disability which can to some extent be alleviated by artificial aids. Many of the sensory methods used in robotics can be applied in attempts to recapture some of the sensory information a partially sighted person has lost. This paper describes a device which uses sonar and stereo vision sensors for this task. The device is portable, and is worn by the user, giving them freedom of movement over kerbs, stairs and rough ground. Sensor motion during walking is measured using visual egomotion recovery and odometry, and has been modelled to allow compensation in the sensor readings. A ground position estimate is continually updated by scene ground-plane fitting, or from the walk-motion model, and is used to classify scene features as obstacles or parts of the ground. Methods for the robust reconstruction of image points and lines into scene features are developed. The recognition of world objects of exceptional significance to a mobile person - kerbs and stairs - is given particular attention. A user interface, which has undergone limited real world testing, is also described. Experimental results are presented for the various parts of the system.

Range finding and feature extraction by segmentation of images for mobile robot navigation

This paper describes techniques to identify various features in a scene using a laser range finding sensor, scanning horizontally in one dimension. The authors describe the optical and electronic properties of the sensor, together with the distributed processing hardware. The authors present a method to recognise and parameterise straight lines and ellipses occurring together in ID range images. In particular, the authors: (1) Present a line fitting system which recursively fits points to line segments, given range and bearing information, and correctly accounts for the sensors error sources. This system also passes on unfitted points to the ellipse fitter. (2) Extend Rothwell and Zissermans improvements to the Bookstein algorithm to determine ellipse parameters from few observed points which lie over a small angular excursion of the ellipse, for example a pipe in an industrial environment. (3) Discuss the problems of outliers, and develop methods of recognising and rejecting them, including the use of intensity as well as range data. The authors show solutions to these problems on real data, and finally discuss further methods they might use to improve the ellipse fitting.

Towards a real-time architecture for obstacle avoidance and path planning in mobile robots

The design and partial implementation of a real-time architecture for a mobile robot, aimed particularly towards a vehicle developed for factory automation, is described. The authors develop a layered design to equip the robot with a number of behavioral competences. They examine sensing and a potential field algorithm especially to achieve modification of behavior at a speed close to the robots operational speed. It is shown how the layered architecture interfaces to the original onboard architecture, which provided sophisticated localization but no ability to deal with environmental exceptions.<<ETX>>

A low-cost system using sparse vision for navigation in the urban environment

Abstract Established mobility aids, such as the long cane, enable visually impaired users to travel safely in urban environments. This paper describes continuing work to enhance this mobility by providing a series of high-level navigational goals. In particular, we describe algorithms to detect doorways and to facilitate centre-path travel. To maintain high performance at low cost, both algorithms use sparsely-sampled images from a single camera. Doorway detection is achieved by the detection of characteristic patterns of near-vertical and near-horizontal lines. The direction of travel along a path is determined by locating the dominant vanishing point of the lines in the image. Experimental results are presented for both algorithms.

A Sensor System for the Navigation of an Underwater Vehicle

A sensor system for an underwater vehicle is described. The vehicle is equipped with inclinometers, gyroscopes, a magnetometer, a pressure gauge, and a sonar system. The sensor models used for the inclinometers and gyroscopes are straightforward; however, the magnetometer can be corrupted by variations in the earth’s field caused by: external objects and internal magnetic fields. We show how to use inclinometer data to adjust for a limited set of external field variation. We also show how to calibrate the magnetometer to compensate for static and thruster-dependent internal fields. The sonar unit uses range differentials between cheap time-of-flight sonar to follow a target. This reduces signal processing since data association is only required on target acquisition, and removes the need to scan an entire landscape, which is usually slow. The gyroscopes are fused via a second indirect filter system. The vehicle attitude is represented as a quaternion; these have a low computational burden, and lack discontinuities and singularities. The simplicity of the indirect filter permits very fast update rates, so that the system may follow rapid vehicle rotations.

Perception of an indoor robot workspace by using CTFM sonar imaging

Ultrasonic sensors have been widely used as time-of-flight range finding systems in mobile robots. Different variations of this scheme lead to robust identification of simple reflector types, like walls, corners and edges. In this paper an alternative approach is attempted, able not only to locate and identify simple reflectors, but to detect and recognize more complicated objects. A more sophisticated sensor, the CTFM sonar, produces an image which corresponds to a range map. The image provides information about the location and type of the reflector. A reflectivity model for planes, corners, and edges is presented and compared with some experimental results. A method to distinguish well structured reflectors from complex objects is then described and the application of the system in a room mapping task is demonstrated.

Robotic Sensing for the Guidance of the Visually Impaired

This paper describes ongoing work into a portable mobility aid, worn by the visually impaired. The system uses stereo vision and sonar sensors for obstacle avoidance and recognition of kerbs. Because the device is carried, the user is given freedom of movement over kerbs, stairs and rough ground, not traversable with a wheeled aid. Motion of the sensor due to the walking action is measured using a digital compass and inclinometer. This motion has been modelled and is tracked to allow compensation of sensor measurements. The vision obstacle detection method uses comparison of image feature disparity with a ground feature disparity function. The disparity function is continually updated by the walk-motion model and by scene ground-plane fitting. Kerb detection is achieved by identifying clusters of parallel lines using the Hough transform. Experimental results are presented from the vision and sonar parts of the system.

Distributed Real-Time Control of a Mobile Robot

ABSTRACTWe describe a distributed real-time architecture, based on transputers, for intelligent control of a mobile robot. This architecture takes the form of a network of sensing and control nodes, based on a novel module that we call Locally Intelligent Control Agents (LICAs). Each LICA has its own processing facility, and the overall system does not require any common buses or central communication facilities. Computation is performed locally and communication occurs between any two LICAs using high speed, point to point communication links. The LICAs run independently to achieve specific subtasks, and in parallel to interact intelligently with the real world. Using this architecture, our mobile robot can navigate in a manufacturing environment, plan an optimal path to reach the goal, and avoid unexpected obstacles in real time. The architecture has been made available commercially for manufacturing applications.

Modeling and classification of rough surfaces using CTFM sonar imaging

The typical use of ultrasonic sensors has been limited to estimation of the location of targets in a robot workspace. CTFM sonars have also been used successfully in classifying primitive targets. In this paper the classification is extended to include textures typical of these found in pathways the robot may need to follow or identify. The pathway classes examined are considered to be plane surfaces of various roughness corresponding to hard smooth floor, carpet, and asphalt. Each class is modeled using an extension of the Kirchhoff approximation method describing the scattering of the acoustic wave on rough surfaces. The CTFM sonar image corresponding to each class is derived and compared with the experimental one. Then a feature is extracted that exploits the differences between the three surface models. A neural network is trained for recognition with excellent results.