Including probabilistic target detection attributes into map representations

Range measuring sensors can play an extremely important role in robot navigation. All range measuring devices rely on a ‘detection criterion’ made in the presence of noise, to determine when the transmitted signal is considered detected and hence a range reading is obtained. In commonly used sensors, such as laser range finders and polaroid sonars, the criterion under which successful detection is assumed, is kept hidden from the user. However, ‘detection decisions’ on the presence of noise still take place within the sensor. This paper integrates signal detection probabilities into the map building process which provides the most accurate interpretation of such sensor data. To facilitate range detection analysis, map building with a frequency modulated continuous wave millimetre wave radar (FMCW MMWR), which is able to provide complete received power-range spectra for multiple targets down range is considered. This allows user intervention in the detection process and although not directly applicable to the commonly used ‘black-box’ type range sensors, provides insight as to how not only range values, but received signal strength values should be incorporated into the map building process.This paper presents two separate methods of map building with sensors which return both range and received signal power information. The first is an algorithm which uses received signal-to-noise power to make an estimates of the range to multiple targets down range, without any signal distribution assumptions. We refer to this as feature detection based on target presence probability (TPP). In contrast to the first method, the second method does use assumptions on the statistics of the signal in target presence and absence scenarios to formulate a probabilistic likelihood detector. This allows for an increased rate of convergence to ground truth. Evidence theory is then introduced to model and update successive observations in a recursive fashion. Both methods are then compared using real MMWR data sets from indoor and outdoor experiments.