Active Sound Localization in a Symmetric Environment

Brindza, Ján; Thomas, Ashleigh; Lee, Spencer; McDermid, W.; He, Yizheng; Lee, Daniel D.

doi:10.5772/56574

Cited by 5 publications

(6 citation statements)

References 14 publications

(15 reference statements)

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“…Several works similar to ours are [ 29 , 30 , 31 ], in that their research focuses on the localization problem in a rectangular, symmetrical environment such as the RoboCup soccer field. In [ 29 ], an approach called Uniform Clustered Particle Filtering MCL is based on Uniform MCL and Clustered Particle Filtering MCL algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…This is to determine which cluster corresponds to the direction of the robot. Brindza et al [ 31 ] propose a solution to the symmetrical field problem that involves the use of sound emission and detection to determine the direction of the defending and attacking goals relative to a robot’s current position, which is used in the RoboCup competition.…”

Section: Related Workmentioning

confidence: 99%

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Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Zhang

Jiang

et al. 2021

Sensors

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Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Zhang

Jiang

et al. 2021

Sensors

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“…It is essential to reduce the noise using noise filter before the recognition. 24 A band-pass filter is carried out in our algorithm and the upper-lower limits are determined by the audio type. For instance, the frequency of whistle is 2.5-3.5 kHz in most cases, so we can set the limits of band-pass filter f L À f H as 1.5-4.5 kHz to filter out the noise.…”

Section: Preprocessing: Noise Filtermentioning

confidence: 99%

Efficient and robust approaches for three-dimensional sound source recognition and localization using humanoid robots sensor arrays

Chen

Liu

Chen

2020

International Journal of Advanced Robotic Systems

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Efficient and robust sound source recognition and localization is one of the basic techniques for humanoid robots in terms of reaction to environments. Due to the fixed sensor arrays and limited computation resources in humanoid robots, there comes challenge for sound source recognition and localization. This article proposes a sound source recognition and localization framework to realize real-time and precise sound source recognition and localization system using humanoid robots’ sensor arrays. The type of the audio is recognized according to the cross-correlation function. And steered response power-phase transform function in discrete angle space is used to search the sound source direction. The sound source recognition and localization framework presents a new multi-robots collaboration system to get the precise three-dimensional sound source position and introduces a distance weighting revision way to optimize the localization performance. Additionally, the experiment results carried out on humanoid robot NAO demonstrate that the proposed approaches can recognize and localize the sound source efficiently and robustly.

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“…In RoboCup SPL, various approaches are proposed to deal with the symmetric field ambiguity, such as building the map outside the field for global orientation estimation by Team rUNSWift [42] and actively emitting a special sound from the keeper by Team UPennalizers [43]. However, instead of developing an additional module, our perception system based on MR-SLAT with MHT can solve the problem by exploiting the moving robots in the field.…”

Section: Robocup Scenario-symmetric Scene Casementioning

confidence: 99%

Exploiting Moving Objects: Multi-Robot Simultaneous Localization and Tracking

Chang

Wang

2016

IEEE Trans. Automat. Sci. Eng.

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Cooperative localization has been proved to effectively outperform single-robot localization. While most of the state-of-the-art multi-robot localization systems either treat moving objects as outliers or accomplish moving object tracking separately from localization, we argue that augmenting moving objects into the localization estimation can further enhance localization performance and is indeed the key to solve several localization challenges such as insufficient map features, no map features, and symmetric maps. In this paper, a multi-robot simultaneous localization and tracking (MR-SLAT) algorithm based on the extended Kalman filter is proposed, and multiple hypothesis tracking (MHT) is integrated into MR-SLAT for dealing with challenging data association issues. The proposed approach is verified in two scenarios: the NAO humanoid robots equipped with cameras and WiFi are used in the RoboCup scenario and the robotic vehicles with laser scanners and dedicated short-range communications (DSRC) are used in the traffic scenario. The experiments with ground truth show that MR-SLAT, by exploiting moving objects, is superior to single-robot localization and cooperative localization in challenging scenarios. Ample experimental and simulation results demonstrate the effectiveness of exploiting moving objects and the generality and feasibility of the proposed MR-SLAT algorithm.Note to Practitioners: Abstract-To apply robot systems in realworld dynamic scenes, the capability of dealing with moving objects is necessary and critical. The key insight of this work is that by exploiting moving objects, many practical localization cases that are challenging for approaches which filter out or separately deal with moving objects can be solved. The main observation is that in the multi-robot scenario, the commonly observed moving objects actually contain valuable information that helps localization. The proposed MR-SLAT algorithm exploits the mutual benefits between localization and tracking by maintaining the correlations between teammate robots and moving objects through the augmented state and thus further improves localization accuracy and reliability. In addition, in the proposed system, data association, which is one of the most critical parts for robot systems to work successfully in practical challenging scenes, is tackled by integrating MHT, and the feasibility is experimentally demonstrated and analyzed. The applicability of the proposed system and the concept of exploiting moving objects are verified through ample experiments in two different scenarios with different platforms and sensors. The manuscript, along with the proposed MR-SLAT approach, accordingly makes contributions to practical multi-agent applications such as intelligent transportation systems.Index Terms-Cooperative systems, multi-sensor systems, robot sensing systems.

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Active Sound Localization in a Symmetric Environment

Cited by 5 publications

References 14 publications

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Efficient and robust approaches for three-dimensional sound source recognition and localization using humanoid robots sensor arrays

Exploiting Moving Objects: Multi-Robot Simultaneous Localization and Tracking

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