Mobile Robot Sonar for Target Localization and Classification

Abstract

A novel sonar array is presented that has applications in mobile robotics for 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 a millimeter and 0.1 degrees 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.The paper establishes that two transmitters and two receivers 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 design approach is that the receivers are closely spaced so as to minimize the correspondence problem of associating 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 templates 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.

Large Scale Sonarray Mapping using Multiple Connected Local Maps

“…The Cartesian coordinates of the reflector are (10) and its covariance relative to local map j is (11) The search bound for m is defined as a circle centred at x, with a radius r BOUND given by (12) Since the position covariance is meant for establishing a search bound for a measurement, not map building, the value of β are not critical. If the bound is so large that it captures more than one map feature for a measurement, the map matching algorithm will eliminate the unsuitable ones with its measure of discrepancy function (section 5.2).…”

“…With the advanced sonar sensor by [12], the robot can very quickly pick up the most salient natural landmarks useful for mapping and localisation. At any instant of navigation, the map, hence the processing demand, is very small.…”

“…Werrimbi has a highly accurate wheel based odometry system. Extereoceptive sensing is performed with an advanced sonarray [12] which can classify reflectors into discrete planes, corners or edges, and accurately estimate their ranges, horizontal bearings and vertical bearings. The above modules are controlled by a 486DX2-66MHz board through a ISA AT Bus.…”

A real time DSP sonar echo processor

“…Sonar signal processing bears similarity to that of RADAR and this is borne out by papers that using RADAR based techniques [3,4,5]. From RADAR theory [6], the minimum variance arrival time estimator in the presence of white gaussian receiver noise is the matched filter -also called template matching.…”

“…This filter is based on finding the peak of the cross correlation of the echo with a priori stored pulse shape templates. This approach has been employed extensively in [3,7,8,9] and no other sonar system is known to perform at greater range or bearing accuracy than the template match approach. Previous template match implementations have been based on transferring complete receiver echo data to a PC.…”