{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:10:20Z","timestamp":1760220620187,"version":"build-2065373602"},"reference-count":14,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2012,3,26]],"date-time":"2012-03-26T00:00:00Z","timestamp":1332720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To enhance sensor capabilities, sensor data readings from different modalities must be fused. The main contribution of this paper is to present a sensor data fusion approach that can reduce KinectTM sensor limitations. This approach involves combining laser with KinectTM sensors. Sensor data is modelled in a 3D environment based on octrees using a probabilistic occupancy estimation. The Bayesian method, which takes into account the uncertainty inherent in the sensor measurements, is used to fuse the sensor information and update the 3D octree map. The sensor fusion yields a significant increase of the field of view of the KinectTM sensor that can be used for robot tasks.<\/jats:p>","DOI":"10.3390\/s120403868","type":"journal-article","created":{"date-parts":[[2012,3,26]],"date-time":"2012-03-26T11:04:58Z","timestamp":1332759898000},"page":"3868-3878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Improvement of KinectTM Sensor Capabilities by Fusion with Laser Sensing Data Using Octree"],"prefix":"10.3390","volume":"12","author":[{"given":"Alfredo","family":"Ch\u00e1vez","sequence":"first","affiliation":[{"name":"\u00c5rhus School of Engineering, \u00c5rhus University Finlandsgade 22, 8200 \u00c5rhus N, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henrik","family":"Karstoft","sequence":"additional","affiliation":[{"name":"\u00c5rhus School of Engineering, \u00c5rhus University Finlandsgade 22, 8200 \u00c5rhus N, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,3,26]]},"reference":[{"key":"ref_1","unstructured":"Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., and Burgard, W. (2010, January 3). OctoMap: A Probabilistic, Flexible, and Compact 3D Map Representation for Robotic Systems. Anchorage, AK, USA."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"de la Puente, P., Rodriguez-Losada, D., Valero, A., and Matia, F. (2009, January 11\u201315). 3D Feature Based Mapping Towards Mobile Robots' Enhanced Performance in Rescue Missions. St. Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354363"},{"key":"ref_3","unstructured":"Hokuyo URG-04LX-UG01. Available online: http:\/\/www.hokuyo-aut.jp\/02sensor\/07scanner\/urg04lxug01.html."},{"key":"ref_4","unstructured":"Agoston, M.K. (2005). Computer Graphics and Geometric Modelling Implementation and Algorithms, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Payeur, P., Hebert, P., Laurendeau, D., and Gosselin, C.M. (, January April). Probabilistic Octree Modeling of a 3D Dynamic Environment. Albuquerque, NM, USA. 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