{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:01:18Z","timestamp":1740135678836,"version":"3.37.3"},"reference-count":16,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2015,3,6]],"date-time":"2015-03-06T00:00:00Z","timestamp":1425600000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>An efficient obstacle detection technique is required so that navigating robots can avoid obstacles and potential hazards. This task is usually simplified by relying on structural patterns. However, obstacle detection constitutes a challenging problem in unstructured unknown environments, where such patterns may not exist. Talukder <jats:italic>et al.<\/jats:italic> (2002, <jats:italic>IEEE Intelligent Vehicles Symposium<\/jats:italic>, pp. 610\u2013618.) successfully derived a method to deal with such environments. Nevertheless, the method has a high computational cost and researchers that employ it usually rely on approximations to achieve real-time. We hypothesize that by using a graphics processing unit (GPU), the computing time of the method can be significantly reduced. Throughout the implementation process, we developed a general framework for processing dynamically-sized sliding windows on a GPU. The framework can be applied to other problems that require similar computation. Experiments were performed with a stereo camera and an RGB-D sensor, where the GPU implementations were compared to multi-core and single-core CPU implementations. The results show a significant gain in the computational performance, i.e. in a particular instance, a GPU implementation is almost 90 times faster than a single-core one.<\/jats:p>","DOI":"10.1017\/s0263574714002914","type":"journal-article","created":{"date-parts":[[2015,3,6]],"date-time":"2015-03-06T01:38:32Z","timestamp":1425605912000},"page":"85-100","source":"Crossref","is-referenced-by-count":1,"title":["Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU"],"prefix":"10.1017","volume":"35","author":[{"given":"Caio C\u00e9sar Teodoro","family":"Mendes","sequence":"first","affiliation":[]},{"given":"Fernando Santos","family":"Os\u00f3rio","sequence":"additional","affiliation":[]},{"given":"Denis Fernando","family":"Wolf","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2015,3,6]]},"reference":[{"key":"S0263574714002914_ref11","doi-asserted-by":"crossref","unstructured":"C. C. T. Mendes , F. S. Osorio and D. F. Wolf , \u201cAn Efficient Obstacle Detection Approach for Organized Point Clouds,\u201d IEEE Intelligent Vehicles Symposium (IV), Gold Coast City, Australia (2013) pp. 1203\u20131208. Available at: http:\/\/dx.doi.org\/10.1109\/IVS.2013.6629630 doi:10.1109\/IVS.2013.6629630.","DOI":"10.1109\/IVS.2013.6629630"},{"key":"S0263574714002914_ref3","doi-asserted-by":"crossref","unstructured":"A. Broggi , C. Caraffi , R. Fedriga and P. Grisleri , \u201cObstacle Detection with Stereo Vision for Off-Road Vehicle Navigation,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition \u2013 Workshops, San Diego, California, USA (2005) p. 65.","DOI":"10.1109\/CVPR.2005.503"},{"key":"S0263574714002914_ref16","doi-asserted-by":"crossref","unstructured":"H. Hirschmuller , \u201cAccurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information,\u201d IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, San Diego, California, USA (2005) pp. 807\u2013814.","DOI":"10.1109\/CVPR.2005.56"},{"key":"S0263574714002914_ref9","doi-asserted-by":"crossref","unstructured":"W. van der Mark , J. van den Heuvel and F. Groen , \u201cStereo Based Obstacle Detection with Uncertainty in Rough Terrain,\u201d IEEE Intelligent Vehicles Symposium, Istanbul, Turkey (2007) pp. 1005\u20131012.","DOI":"10.1109\/IVS.2007.4290248"},{"key":"S0263574714002914_ref6","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45118-8_50"},{"key":"S0263574714002914_ref10","unstructured":"P. Santana , P. Santos , L. Correia and J. Barata , \u201cCross-Country Obstacle Detection: Space-Variant Resolution and Outliers Removal,\u201d IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008) pp. 1836\u20131841."},{"key":"S0263574714002914_ref14","unstructured":"S. Xiao and W. chun Feng , \u201cInter-Block GPU Communication Via Fast Barrier Synchronization,\u201d IEEE International Symposium on Parallel Distributed Processing, Atlanta, Georgia, USA (2010) pp. 1\u201312."},{"key":"S0263574714002914_ref12","doi-asserted-by":"publisher","DOI":"10.1023\/B:AURO.0000047286.62481.1d"},{"key":"S0263574714002914_ref8","unstructured":"A. Broggi , M. Buzzoni , M. Felisa and P. Zani , \u201cStereo Obstacle Detection in Challenging Environments: The viac Experience,\u201d IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Francisco, California, USA (2011) pp. 1599\u20131604."},{"key":"S0263574714002914_ref1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20276"},{"key":"S0263574714002914_ref5","doi-asserted-by":"crossref","unstructured":"J. Kolter , M. Rodgers and A. Ng , \u201cA Control Architecture for Quadruped Locomotion Over Rough Terrain,\u201d IEEE International Conference on Robotics and Automation, Pasadena, California, USA (2008) pp. 811\u2013818.","DOI":"10.1109\/ROBOT.2008.4543305"},{"key":"S0263574714002914_ref13","unstructured":"NVIDIA, OpenCL Programming Guide for the CUDA Architecture (May 2010). Available at: http:\/\/developer.download.nvidia.com\/compute\/cuda\/3_1\/toolkit\/docs\/NVIDIA_OpenCL_ProgrammingGuide.pdf"},{"key":"S0263574714002914_ref15","unstructured":"AMD, AMD Accelerated Parallel Processing OpenCL Programming Guide (Jul 2013). Available at: http:\/\/developer.amd.com\/tools\/hc\/AMDAPPSDK\/assets\/AMD_Accelerated_Parallel_Processing_OpenCL_Programming_Guide.pdf."},{"key":"S0263574714002914_ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2007.908583"},{"key":"S0263574714002914_ref7","unstructured":"A. Talukder , R. Manduchi , A. Rankin and L. Matthies , \u201cFast and Reliable Obstacle Detection and Segmentation for Cross-Country Navigation,\u201d IEEE Intelligent Vehicles Symposium, Versailles, France (2002) pp. 610\u2013618."},{"key":"S0263574714002914_ref2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20271"}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574714002914","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T01:14:03Z","timestamp":1555550043000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574714002914\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,3,6]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["S0263574714002914"],"URL":"https:\/\/doi.org\/10.1017\/s0263574714002914","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"type":"print","value":"0263-5747"},{"type":"electronic","value":"1469-8668"}],"subject":[],"published":{"date-parts":[[2015,3,6]]}}}