arXiv subjects

Georg K. J. Fischer

Publications and source records attributed to Georg K. J. Fischer.

4 recordsLinked to original sources

arXiv2025

MASSLOC: A Massive Sound Source Localization System based on Direction-of-Arrival Estimation

Acoustic indoor localization offers the potential for highly accurate position estimation while generally exhibiting low hardware requirements compared to Radio Frequency (RF)-based solutions. Furthermore, angular-based localization significantly reduces installation effort by minimizing the number of required fixed anchor nodes. In this contribution, we propose the so-called MASSLOC system, which leverages sparse two-dimensional array geometries to localize and identify a large number of concurrently active sources. Additionally, the use of complementary Zadoff-Chu sequences is introduced to enable efficient, beamforming-based source identification. These sequences provide a trade-off between favorable correlation properties and accurate, unsynchronized direction-of-arrival estimation by exhibiting a spectrally balanced waveform. The system is evaluated in both a controlled anechoic chamber and a highly reverberant lobby environment with a reverberation time of 1.6 s. In a laboratory setting, successful direction-of-arrival estimation and identification of up to 14 simultaneously emitting sources are demonstrated. Adopting a Perspective-n-Point (PnP) calibration approach, the system achieves a median three-dimensional localization error of 55.7 mm and a median angular error of 0.84 deg with dynamic source movement of up to 1.9 mps in the challenging reverberant environment. The multi-source capability is also demonstrated and evaluated in that environment with a total of three tags. These results indicate the scalability and robustness of the MASSLOC system, even under challenging acoustic conditions.

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arXiv2024

Evaluation of Sparse Acoustic Array Geometries for Indoor Localization

Angle-of-Arrival estimation technology, with its potential advantages, emerges as an intriguing choice for indoor localization. Notably, it holds the promise of reducing installation costs. In contrast to ToF/TDoA based systems, AoA-based approaches require a reduced number of nodes for effective localization. This characteristic establishes a trade-off between installation costs and the complexity of hardware and software. Moreover, the appeal of acoustic localization is further heightened by its capacity to provide cost-effective hardware solutions while maintaining a high degree of accuracy. Consequently, acoustic AoA estimation technology stands out as a feasible and compelling option in the field of indoor localization. Sparse arrays additionally have the ability to estimate the DoA of more sources than available sensors by placing sensors in a specific geometry. In this contribution, we introduce a measurement platform designed to evaluate various sparse array geometries experimentally. The acoustic microphone array comprises 64 microphones arranged in an 8x8 grid, following an Uniform Rectangular Array (URA) configuration, with a grid spacing of 8.255 mm. This configuration achieves a spatial Nyquist frequency of approximately 20.8 kHz in the acoustic domain at room temperature. Notably, the array exhibits a mean spherical error of 1.26° when excluding higher elevation angles. The platform allows for masking sensors to simulate sparse array configurations. We assess four array geometries through simulations and experimental data, identifying the Open-Box and Nested array geometries as robust candidates. Additionally, we demonstrate the array's capability to concurrently estimate the directions of three emitting sources using experimental data, employing waveforms consisting of orthogonal codes.

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arXiv2024

Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision

Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing this need, we introduce an autonomously navigating robotic system designed for comprehensive plant inspection. This innovative system comprises a robotic platform equipped with a diverse array of sensors integrated to facilitate the detection of various process and infrastructure parameters. These sensors encompass optical (LiDAR, Stereo, UV/IR/RGB cameras), olfactory (electronic nose), and acoustic (microphone array) capabilities, enabling the identification of factors such as methane leaks, flow rates, and infrastructural anomalies. The proposed system underwent individual evaluation at a wastewater treatment site within a chemical plant, providing a practical and challenging environment for testing. The evaluation process encompassed key aspects such as object detection, 3D localization, and path planning. Furthermore, specific evaluations were conducted for optical methane leak detection and localization, as well as acoustic assessments focusing on pump equipment and gas leak localization.

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arXiv2023

A Smart Robotic System for Industrial Plant Supervision

In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions. To alleviate their task, we present a system consisting of an autonomously navigating robot integrated with various sensors and intelligent data processing. It is able to detect methane leaks and estimate its flow rate, detect more general gas anomalies, recognize oil films, localize sound sources and detect failure cases, map the environment in 3D, and navigate autonomously, employing recognition and avoidance of dynamic obstacles. We evaluate our system at a wastewater facility in full working conditions. Our results demonstrate that the system is able to robustly navigate the plant and provide useful information about critical operating conditions.

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