Publication type: Conferences

Scan Matching (SM) is a technique to estimate the robot pose by computing the overlap between two successive range scans. Several SM algorithms have been developed in last decades and, although they work well enough with laser data, they are less powerful when treating data from ultrasonic sensors, which are more scattered and noisy. In this paper we present a new SM algorithm whose main contribution is a time measurement instead of distance between the scan data points, particularly focused on ultrasonic applications.

Nearly all advanced mobile robotic tasks require some knowledge of the robot location in the environment. Autonomous Underwater Vehicles are usually endowed with acoustic devices to perform localization, because underwater scenarios pose important limitations to light based sensors. One of these acoustic devices is the Mechanically Scanned Imaging Sonar (MSIS). This sensor scans the environment by emitting ultrasonic pulses and it provides echo intensity profiles (beams) of the scanned area. Our goal is to obtain range scans instead of the beams as they are provided by the MSIS. Accordingly, the proposal of this paper is to process the acoustic images in order to compute accurate distances from the sensor to the relevant obstacles in the beam. These range scans are suitable to be used in scan matching, SLAM or other approaches to estimate the robot pose.

Likelihood fields (LF) have been used in the past to perform localization. These approaches infer the LF from range data. However, an underwater Mechanically Scanned Imaging Sonar (MSIS) does not provide distances to the closest obstacles but echo intensity profiles. In this case, obtaining ranges involves processing the acoustic data.
The proposal in this paper avoids the range extraction to build the LF. Instead of processing the acoustic images to obtain ranges and then using these ranges to infer a LF, this paper proposes the use of the acoustic image itself as a good approximation of the LF. The experimental results show the potential benefits of using this idea to define a measurement model to perform mobile robot localization.

This paper proposes a framework to perform Simultaneous Localization and Mapping (SLAM) using the scans gathered by a Mechanically Scanned Imaging Sonar (MSIS). To this end, the acoustic profiles provided by the MSIS are processed to obtain range data. Also, dead reckoning is used to compensate the robot motion during the sonar mechanical scanning and build range scans. When a new scan is constructed, its estimated position with respect to the previously gathered one is used to augment the SLAM state vector. Also, each new scan is matched against the previously detected ones by means of scan matching techniques. As the state vector contains relative positions between consecutively gathered scans, the measurement update explicitly takes into account the robot trajectory involved in each loop closure.

Underwater environments are extremely challenging to perform localization. Autonomous Underwater Vehicles (AUV) are usually endowed with acoustic devices such as a Mechanically Scanned Imaging Sonar (MSIS). This sensor scans the environment by emitting ultrasonic pulses and it provides echo intensity profiles of the scanned area. Our goal is to provide self-localization capabilities to an AUV endowed with a MSIS. To this end, this paper proposes a scan matching strategy to estimate the robot motion. This strategy extracts range information from the sensor data, deals with the large scan times and performs a probabilistic data association. The proposal is tested with real data obtained during a trip in a marina environment, and the results show the benefits of our proposal by comparing it to other well known approaches.