Existing sonar rings are limited in their refresh rate to the transmit echo rate, that is, waiting for maximum range echoes to arrive before transmitting again. This paper presents a sonar ring refreshing at 60 Hz for 5.7-m range, which is twice the transmit echo rate, and this leads to lower latency, denser measurements. Two custom Field Programmable Gate Array signal processors provide real time continuous match filtering with dynamic templates. A new method is implemented to select the transmit time from a random set based on minimizing interference. Experiments demonstrate the increased refresh rate, interference rejection, and maps generated by the sonar ring.

Matched filtering optimally estimates the arrival time for a sonar sensor by correlating received signals with templates. This paper presents a sonar ring with continuous matched filtering on 48 receiver channels sampled at 500kHz. The design dynamically switches the matched filter templates to account for pulse shape variations with range. To achieve real-time, low-latency and optimal performance, processing is implemented on an field-programmable gate array (FPGA) transmitting sonar pulses (2 periods of a 45kHz sine wave) at repetition rate of 30-Hz to 5.7-m range. The paper describes the removal of secondary peaks of the correlation output of matched filtering and template selection. Results include sonar maps, accuracy measurements and localization of weak targets.

An algorithm for three-level hierarchical sensor fusions has beenproposed and applied to environment map building with enhanced accuracy and efficiency. The algorithm was realized through the two new types of sensor modules, which are composed of a halogen lamp-based active vision sensor and a semicircular ultrasonic (US) and infrared (IR) sensor system. In the first-level fusion, the US and IR sensor information is utilized in terms of the geometric characteristics of the sensor location. In the second-level fusion, the outputs from the US and IR sensors are combined with the sheet of halogen light through a proposed rule base. In the third-level fusion, local maps from the first- and second-level fusion are updated in a probabilistic way for a very accurate environment local map. A practical implementation has been carried out to demonstrate the efficiency and accuracy of the proposed hierarchical sensor fusion algorithm in environment map building.

An autonomous mobile robot operating in an unknown indoor environment often needs to map the environment while localizing within the map. Feature-based world models including line and point features are widely used by researchers. This paper presents a novel delayed-classi-fication algorithm to categorize these features using a recently developed high-performance sonar ring within a simultaneous localization and map-building (SLAM) process. The sonar ring sensor accurately measures range and bearing to multiple targets at near real-time repetition rates of 11.5 Hz to 6 m range, and uses 24 simultaneously fired transmitters, 48 receivers and multiple echoes per receiver. The proposed algorithm is based on hypothesis generation and verification using the advanced sonar ring data and an extended Kalman filter (EKF) approach. It is capable of initiating new geometric features and classifying them within a short distance of travel of about 10 cm. For each new sonar reading not matching an existing feature, we initiate a pair of probational line and point features resulting from accurate range and bearing measurements. Later measurements are used to confirm or remove the probational features using EKF validation gates. The odometry error model of the filter allows for variations in effective wheel separation required by pneumatic robot tyres. The implementation of the novel classification and SLAM algorithm is discussed in this paper and experimental results using real sonar data are presented.

We present a feature-based probabilistic map building algorithm which directly utilizes time and amplitude information of sonar in indoor environments. Utilizing additional amplitude-of-signal (AOS) obtained concurrently with time-of-flight (TOF), the amount of inclination of target can be directly calculated from a single echo, and the number of measurements can be greatly reduced with result similar to dense scanning. A set of target groups (set of hypothesized targets originated from one measurement) is used and refined by each measurement using an extended Kalman filter and Bayesian conditional probability. Experimental results in a real indoor environment are presented to show the validity of our algorithm.