[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128321-en":3,"doc-seo-128321-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128321,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Framework for Acoustic Reflector Mapping - Research Proposal and Evaluation","Sonar-based indoor mapping is a long-standing robotics approach, yet noise can rapidly degrade acoustic sensing reliability and limits broader adoption against cameras and lidars. This paper presents a machine learning framework that improves traditional echolocation mapping by suppressing background-noise effects, removing outliers and artefacts from generated maps, and enhancing robustness in reverberant conditions. Simulated results show stable operation at an SNR of −10 dB and effective mapping of simulated room outlines on a robotic platform.","A machine learning framework for acoustic re􀀃ector mapping  \nUsama Saqib 1 , Letizia Marchegiani2 , and Jesper Rindom Jensen 3  \narXiv :2409 . 12094v1 [ cs .RO] 18 Sep 2024  \nAbstract—Sonar-based indoor mapping systems have been widely employed in robotics for several decades. While such systems are still the mainstream in underwater and pipe inspection settings, the vulnerability to noise reduced, over time, their general widespread usage in favour of other modalities(e.g., cameras, lidars), whose technologies were encountering, instead, extraordinary advancements. Nevertheless, mapping physical environments using acoustic signals and echolocation can bring signi􀀂cant bene􀀂ts to robot navigation in adverse scenarios, thanks to their complementary characteristics compared to other sensors. Cameras and lidars, indeed, struggle in harsh weather conditions, when dealing with lack of illumination, or with non-re􀀃ective walls. Yet, for acoustic sensors to be able to generate accurate maps, noise has to be properly and effectively handled. Traditional signal processing techniques are not always a solution in those cases. In this paper, we propose a framework where machine learning is exploited to aid more traditional signal processing methods to cope with background noise, by removing outliers and artefacts from the generated maps using acoustic sensors. Our goal is to demonstrate that the performance of traditional echolocation mapping techniques can be greatly enhanced, even in particularly noisy conditions, facilitating the employment of acoustic sensors in state-ofthe-art multi-modal robot navigation systems. Our simulated evaluation demonstrates that the system can reliably operate at an SNR of 􀀀10dB. Moreover, we also show that the proposed method is capable of operating in different reverberate environments. In this paper, we also use the proposed method to map the outline of a simulated room using a robotic platform.  \nI. INTRODUCTION  \nSimultaneous localization and mapping (SLAM) algorithms predominately rely on sensors such as cameras and lasers to build maps of the environment and localise against those maps. Yet, such systems are vulnerable to speci􀀂c conditions: e.g., cameras struggle in case of low visibility, and lidars suffer in harsh weather. Furthermore, these technologies encounter dif􀀂culties when handling re􀀃ective objects and surfaces [1] . This limits their applicability in constructing spatial maps of certain indoor environments, e.g., store centres, where glass doors and shop windows are ubiquitous.  \nEcholocation has been extensively studied in the past, leading to the development of acoustics-based mapping systems [2], [3] . Such systems are not affected by the environment’s appearance, weather or lighting conditions, but their performance can decrease abruptly in the presence of  \n*This work was not supported by any organization  \n1Author is an independent researcher based in Denmark. [usamasaqib@gmail.com](usamasaqib@gmail.com)  \n2Authors are with Department of Engineering and Architecture, University of Parma, Italy letizia .marchegiani@unipr .it  \n3Author are with Audio Analysis Lab, Department of Electrical Systems, Aalborg University, Denmark [jr j@es.aau.dk](jr j@es.aau.dk)  \nnoise [1], [4]. Acoustic sensors have, indeed, complementary characteristics compared to the above-mentioned sensors, which make them a precious asset in the development of robust and reliable multi-modal mapping systems. Additionally, acoustics-based mapping modules are computationally much lighter than vision and laser-based ones, making them particularly convenient for resource-constrained robots (e.g., drones) [5] . In particular, we opted for the use of audible signals and microphones, as, compared to other proximity sensors (e.g. ultrasound, infrared), has some bene􀀂ts when operating in the speci􀀂c settings we are interested in. Firstly, the variety of polar patterns of microphones allows the use of less directional ones,","cbCaibHANcN2ae8u","https://ap.wps.com/l/cbCaibHANcN2ae8u","pdf",209660,2,1,7,"English","en",105,"# Abstract\n# Introduction\n## Motivation for acoustic mapping\n## Limitations of TOA/DOA estimators\n## Proposed framework overview\n# Related work and prior solutions","[{\"question\":\"Why do acoustic mapping systems need noise handling?\",\"answer\":\"Accurate mapping with acoustic sensors requires effective treatment of background noise. Traditional signal processing may not be sufficient when noise and artefacts distort the generated maps.\"},{\"question\":\"What is the proposed framework designed to achieve?\",\"answer\":\"The framework uses machine learning to support traditional signal processing, aiming to remove outliers and artefacts and improve echolocation mapping performance, even in very noisy conditions.\"},{\"question\":\"How is the method evaluated and what performance is reported?\",\"answer\":\"The paper reports a simulated evaluation demonstrating reliable operation at an SNR of −10 dB and successful operation across different reverberant environments, including outlining a simulated room using a robotic platform.\"}]","A Machine Learning Framework for Acoustic Reflector Mapping - Research Proposal and Evaluation | PDF",1785946825,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-machine-learning-framework-for-acoustic-reflector-mapping-research-proposal-and-evaluation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/a-machine-learning-framework-for-acoustic-reflector-mapping-research-proposal-and-evaluation/128321/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why do acoustic mapping systems need noise handling?","Question",{"text":76,"@type":77},"Accurate mapping with acoustic sensors requires effective treatment of background noise. Traditional signal processing may not be sufficient when noise and artefacts distort the generated maps.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the proposed framework designed to achieve?",{"text":81,"@type":77},"The framework uses machine learning to support traditional signal processing, aiming to remove outliers and artefacts and improve echolocation mapping performance, even in very noisy conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the method evaluated and what performance is reported?",{"text":85,"@type":77},"The paper reports a simulated evaluation demonstrating reliable operation at an SNR of −10 dB and successful operation across different reverberant environments, including outlining a simulated room using a robotic platform.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]