Antoni Burguera

On the use of likelihood fields to perform sonar scan matching localization

Scan matching algorithms have been extensively used in the last years to perform mobile robot localization. Although these algorithms require dense and accurate sets of readings with which to work, such as the ones provided by laser range finders, different studies have shown that scan matching localization is also possible with sonar sensors. Both sonar and laser scan matching algorithms are usually based on the ideas introduced in the ICP (Iterative Closest Point) approach. In this paper a different approach to scan matching, the Likelihood Field based approach, is presented. Three scan matching algorithms based on this concept, the non filtered sNDT (sonar Normal Distributions Transform), the filtered sNDT and the LF/SoG (Likelihood Field/Sum of Gaussians), are introduced and analyzed. These algorithms are experimentally evaluated and compared to previously existing ICP-based algorithms. The obtained results suggest that the Likelihood Field based approach compares favorably with algorithms from the ICP family in terms of robustness and accuracy. The convergence speed, as well as the time requirements, are also experimentally evaluated and discussed.

A Probabilistic Framework for Sonar Scan Matching Localization

Scan matching is a popular localization technique based on comparing two sets of range readings gathered at consecutive robot poses. Scan matching algorithms implicitly assume that matching readings correspond to the same object in the environment. This is a reasonable assumption when using accurate sensors such as laser range finders and that is why they are extensively used to perform scan matching localization. However, when using other sensors such as ultrasonic range finders or visual sonar, this assumption is no longer valid because of their lower angular resolution and the sparsity of the readings. In this paper we present a sonar scan matching framework, the spIC, which is able to deal with the sparseness and low angular resolution of sonar sensors. To deal with sparseness, a process to group sonar readings gathered along short robot trajectories is presented. Probabilistic models of ultrasonic and odometric sensors are defined to cope with the low sonar angular resolution. Consequently, a probabilistic scan matching process is performed. Finally, the correction of the whole robot trajectory involved in the matching process is presented as a constrained optimization problem.

Sonar Sensor Models and Their Application to Mobile Robot Localization

This paper presents a novel approach to mobile robot localization using sonar sensors. This approach is based on the use of particle filters. Each particle is augmented with local environment information which is updated during the mission execution. An experimental characterization of the sonar sensors used is provided in the paper. A probabilistic measurement model that takes into account the sonar uncertainties is defined according to the experimental characterization. The experimental results quantitatively evaluate the presented approach and provide a comparison with other localization strategies based on both the sonar and the laser. Some qualitative results are also provided for visual inspection.

Probabilistic Sonar Scan Matching for Robust Localization

This paper presents a probabilistic framework to perform scan matching localization using standard time-of-flight ultrasonic sensors. Probabilistic models of the sensors as well as techniques to propagate the errors through the models are also presented and discussed. A method to estimate the most probable trajectory followed by the robot according to the scan matching and odometry estimations is also presented. Thanks to that, accurate robot localization can be performed without the need of geometric constraints. The experiments demonstrate the robustness of our method even in the presence of large amounts of noisy readings and odometric errors.

Robust scan matching localization using ultrasonic range finders

The work presented in this paper deals with scan matching localization using ultrasonic range sensors. Our contribution resides in the extension of ICP based algorithms to be used with ultrasonic sensor data. This extension consists of a pre-process step, where ultrasonic sensor readings are grouped to overcome their sparseness, and a post-process step, where the whole robot trajectory involved in the grouping process is corrected. Thanks to that a great improvement with respect to odometry is obtained. Experimental results show that even huge odometric errors are corrected with the presented method.

Scan-based SLAM with trajectory correction in underwater environments

This paper presents an approach to perform Simultaneous Localization and Mapping (SLAM) in underwater environments using a Mechanically Scanned Imaging Sonar (MSIS) not relying on the existence of features in the environment. The proposal has to deal with the particularities of the MSIS in order to obtain range scans while correcting the motion induced distortions. The SLAM algorithm manages the relative poses between the gathered scans, thus making it possible to correct the whole Autonomous Underwater Vehicle (AUV) trajectories involved in the loop closures. Additionally, the loop closures can be delayed if needed. The experiments are based on real data obtained by an AUV endowed with an MSIS, a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). Also, GPS data is available as a ground truth. The results show the quality of our approach by comparing it to GPS and to other previously existing algorithms.

A solution for integrating map building and self localization strategies in mobile robotics

For a mobile robot to execute useful missions it has to be endowed with navigation ability. To navigate successfully, information about the environment and the robot position is needed. This article presents a robust system for detecting, identifying, analyzing, and comparing environmental landmarks obtained from standard range sensors and using them to generate and incrementally improve a hybrid topological‐metric map of the environment. This map is used in turn to correct the estimation of the robot position. The control architecture that integrates this mapping and localization system is described, as well as the experimental results obtained on both a simulated robot and a real one.

The UspIC: Performing Scan Matching Localization Using an Imaging Sonar

This paper presents a novel approach to localize an underwater mobile robot based on scan matching using a Mechanically Scanned Imaging Sonar (MSIS). When used to perform scan matching, this sensor presents some problems such as significant uncertainty in the measurements or large scan times, which lead to a motion induced distortion. This paper presents the uspIC, which deals with these problems by adopting a probabilistic scan matching strategy and by defining a method to strongly alleviate the motion induced distortion. Experimental results evaluating our approach and comparing it to previously existing methods are provided.

Underwater SLAM with robocentric trajectory using a mechanically scanned imaging sonar

This paper proposes a novel approach to perform underwater Simultaneous Localization and Mapping (SLAM) using a Mechanically Scanned Imaging Sonar (MSIS). This approach starts by processing the MSIS data in order to obtain range scans while taking into account the robot motion. Then, the relative motions between consecutively gathered scans are stored in the state vector. Thus, the whole sequence of robot motions between gathered scans is used to perform SLAM using an Extended Kalman Filter (EKF). One of the novelties is that this sequence is not represented with respect to a world-fixed coordinate frame, but with respect to a coordinate frame locked to the robot. Thanks to this, EKF linearization errors are reduced. The experimental results in underwater environments validate the proposal comparing the new robocentric approach to the world-centric trajectory method.

Underwater Scan Matching using a Mechanical Scanned Imaging Sonar

Abstract 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.