{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T12:18:12Z","timestamp":1771935492945,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T00:00:00Z","timestamp":1613347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["No.2017YFB1301203"],"award-info":[{"award-number":["No.2017YFB1301203"]}]},{"name":"National Key R&amp;D Program of China","award":["No.2017YFB1301202"],"award-info":[{"award-number":["No.2017YFB1301202"]}]},{"name":"Key R&amp;D Program of Zhejiang Province","award":["No.2021C01065"],"award-info":[{"award-number":["No.2021C01065"]}]},{"name":"Key R&amp;D Program of Zhejiang Province","award":["No.2020C01025"],"award-info":[{"award-number":["No.2020C01025"]}]},{"name":"Ningbo Science and technology project","award":["No.2018B10099"],"award-info":[{"award-number":["No.2018B10099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation of room maps is an essential issue in mobile robots\u2019 execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.<\/jats:p>","DOI":"10.3390\/s21041365","type":"journal-article","created":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T05:52:49Z","timestamp":1613368369000},"page":"1365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps"],"prefix":"10.3390","volume":"21","author":[{"given":"Tao","family":"Zheng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Zhizhao","family":"Duan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Guodong","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Shengjie","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Zhiyong","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mozos, \u00d3.M. 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