Lindsay Kleeman

Mobile robot sonar for target localization and classification

A novel sonar array is presented that has applications in mobile robotics for localization and mapping of indoor en vironments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8m. By accounting for effects of temperature and humidity, the sys tem is accurate to within 1 mm and 0.1° in still air. Targets separated by 10 mm in range can be discriminated. The error covariance matrix for these measurements is derived to allow fusion with other sensors. Targets are statistically classified into four reflector types: planes, corners, edges, and unknown. This article establishes that two transmitters and two re ceivers are necessary and sufficient to distinguish planes, corners, and edges. A sensor array is presented with this minimum number of transmitters and receivers. A novel de sign approach is used such that the receivers are closely spaced so as to minimize the correspondence problem of as sociating different receiver echoes from multiple targets. A linear filter model for pulse transmission, reception, air absorption, and dispersion is used to generate a set of tem plates for the echo as a function of range and bearing angle. The optimal echo arrival time is estimated from the maximum cross-correlation of the echo with the templates. The use of templates also allows overlapping echoes and disturbances to be rejected. Noise characteristics are modeled for use in the maximum likelihood estimates of target range and bearing. Experimental results are presented to verify assumptions and characterize the sensor.

Accurate odometry and error modelling for a mobile robot

This paper presents the key steps involved in the design, calibration and error modelling of a low cost odometry system capable of achieving high accuracy dead-reckoning. A consistent error model for estimating position and orientation errors has been developed. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark test. Experimental results show that, despite its low cost, our systems performance, with regard to dead-reckoning accuracy, is comparable to some of the best reported odometry vehicle.

Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning

An active beacon localization system that estimates position and heading for a mobile robot is described. An iterated extended Kalman filter was applied to the beacon and dead-reckoning data to estimate optimal values of position and heading, given a model for the localizer and robot motion. The author describes the implementation and experimental results of the localization system. Position and heading angle updates were calculated in real time every 150 ms with a measured standard deviation of path error of 40 mm in a 12 m/sup 2/ workspace.<<ETX>>

Laser scan matching in polar coordinates with application to SLAM

This paper presents a novel method for 2D laser scan matching called polar scan matching (PSM). The method belongs to the family of point to point matching approaches. Our method avoids searching for point associations by simply matching points with the same bearing. This association rule enables the construction of an algorithm faster than the iterative closest point (ICP). Firstly the PSM approach is tested with simulated laser scans. Then the accuracy of our matching algorithm is evaluated from real laser scans from known relative positions to establish a ground truth. Furthermore, to demonstrate the practical usability of the new PSM approach, experimental results from a Kalman filter implementation of simultaneous localization and mapping (SLAM) are provided.

Fast Laser Scan Matching using Polar Coordinates

In this paper a novel Polar Scan Matching (PSM) approach is described that works in the laser scanners polar coordinate system, therefore taking advantage of the structure of the laser measurements and eliminating the need for an expensive search for corresponding points in other scan match approaches. PSM belongs to the family of point to point scan matching approaches with its matching bearing association rule. The performance of PSM is thoroughly evaluated in a simulated experiment, in experiments using ground truth, in experiments aimed at determining the area of convergence and in a SLAM experiment. All results are compared to results obtained using an iterated closest point (ICP) scan matching algorithm implementation. It is found that PSM is superior to the ICP implementation in processing speed and that PSM converges to a correct solution from a larger range of initial positions.

Advanced sonar and laser range finder fusion for simultaneous localization and mapping

Increasing the information content of measurements can ease some of the problems associated with simultaneous localization and mapping (SLAM). We present an approach for combining measurements from a laser range finder with measurements from an advanced sonar array capable of accurate range and bearing measurements and edge, corner and plane classification. In our approach sonar aids laser segmentation, laser aids good sonar point feature selection and laser and sonar measurements of the same object are fused. We also present a novel approach for fitting right angle corners to laser range data, which enables simple error estimation through the minimization of sum of square range residuals. The results are then used for SLAM with a mobile robot.

Interactive SLAM using Laser and Advanced Sonar

This paper presents a novel approach to mapping for mobile robots that exploits user interaction to semiautonomously create a labelled map of the environment. The robot autonomously follows the user and is provided with a verbal commentary on the current location with phrases such as “Robot, we are in the office”. At the same time, a metric feature map is generated using fusion of laser and advanced sonar measurements in a Kalman filter based SLAM framework, which is later used for localization. When mapping is complete, the robot generates an occupancy grid for use in global task planning. The occupancy grid is created using a novel laser scan registration scheme that relies on storing the path of the robot along with associated local SLAM features during mapping, and later recovering the path by matching the associated local features to the final SLAM map. The occupancy grid is segmented into labelled rooms using an algorithm based on watershed segmentation and integration of the verbal commentary. Experimental results demonstrate our mobile robot creating SLAM and segmented occupancy grid maps of rooms along a 70 metre corridor, and then using these maps to navigate between rooms.

An optimal sonar array for target localization and classification

A novel sonar array for mobile robots is presented with applications to localization and mapping of indoor environments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8 meters. By accounting for effects of temperature and humidity, the system is accurate to within 1 mm and 6.1 degrees in still air. Targets separated by 10 mm can be discriminated. Targets are classified into planes, corners, edges and unknown, with the minimum of two transmitters and two receivers. A novel approach is that receivers are closely spaced to minimize the correspondence problem of associating echoes from multiple targets. A set of templates is generated for echoes to allow the optimal arrival time to be estimated, and overlapping echoes and disturbances to be rejected.<<ETX>>

Robust Appearance Based Visual Route Following for Navigation in Large-scale Outdoor Environments

In this paper we present a navigation algorithm that enables mobile robots to retrace routes previously taught under the control of human operators in outdoor environments. Possible applications include robot couriers, autonomous vehicles, tour guides and robotic patrols. The appearance-based approach presented in the paper is provably convergent, computationally inexpensive compared with map-based approaches and requires only odometry and a monocular omnidirectional vision sensor. A sequence of reference images is recorded during the human-guided route-teaching phase. Before starting the autonomous phase, the robot needs to be positioned at the beginning of the route. During the autonomous phase, the measurement image is compared with reference images using image cross-correlation performed in the Fourier domain to recover the difference in relative orientation. Route following is achieved by compensating for this orientation difference. Over 18 km of experiments performed under varying conditions demonstrate the algorithms robustness to lighting variations and partial occlusion. Obstacle avoidance is not included in the current system.

A sonar sensor for accurate 3D target localisation and classification

This paper presents a novel sonar sensor consisting of three transmitters and three receivers that can localise and classify 3D targets into 16 different naturally occurring indoor classes. The sensor produces submillimeter range and sub-degree bearing accuracies using an optimal matched filter time of flight estimator up to a range of 6 meters. The sensor configuration, hardware and processing are described. Experimental results from the sensor are presented.