{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:17:46Z","timestamp":1771467466427,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T00:00:00Z","timestamp":1661817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["2021\/395"],"award-info":[{"award-number":["2021\/395"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images. This work proposes an efficient obstacle detection and tracking method using depth images to facilitate quick dynamic obstacle detection. To achieve early detection of dynamic obstacles and stable estimation of their states, as in previous methods, we applied a u-depth map for obstacle detection. Unlike existing methods, the present method provides dynamic thresholding facilities on the u-depth map to detect obstacles more accurately. Here, we propose a restricted v-depth map technique, using post-processing after the u-depth map processing to obtain a better prediction of the obstacle dimension. We also propose a new algorithm to track obstacles until they are within the field of view (FOV). We evaluate the performance of the proposed system on different kinds of data sets. The proposed method outperformed the vision-based state-of-the-art (SoA) methods in terms of state estimation of dynamic obstacles and execution time.<\/jats:p>","DOI":"10.3390\/s22176537","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:13:56Z","timestamp":1661904836000},"page":"6537","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8017-171X","authenticated-orcid":false,"given":"Arindam","family":"Saha","sequence":"first","affiliation":[{"name":"Department of Information Technology, Jadavpur University, Kolkata 700098, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3731-0005","authenticated-orcid":false,"given":"Bibhas Chandra","family":"Dhara","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Jadavpur University, Kolkata 700098, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1476-041X","authenticated-orcid":false,"given":"Saiyed","family":"Umer","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Aliah University, Kolkata 700156, India"}]},{"given":"Kulakov","family":"Yurii","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, National Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d, 03056 Kyiv, Ukraine"}]},{"given":"Jazem Mutared","family":"Alanazi","sequence":"additional","affiliation":[{"name":"Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8477-8319","authenticated-orcid":false,"given":"Ahmad Ali","family":"AlZubi","sequence":"additional","affiliation":[{"name":"Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, S., and Xiao, J. 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