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Typically these sensors have a fixed angular resolution and field of view (FOV). This is in contrast to human perception, which involves foveating: scanning with the eyes\u2019 highest angular resolution over regions of interest (ROIs). We build a scanning depth sensor that can control its angular resolution over the FOV. This opens up new directions for robotics research, because many algorithms in localization, mapping, exploration, and manipulation make implicit assumptions about the fixed resolution of a depth sensor, impacting latency, energy efficiency, and accuracy. Our algorithms increase resolution in ROIs either through deconvolutions or intelligent sample distribution across the FOV. The areas of high resolution in the sensor FOV act as artificial fovea and we adaptively vary the fovea locations to maximize a well-known information theoretic measure. We demonstrate novel applications such as adaptive time-of-flight (TOF) sensing, LiDAR zoom, gradient-based LiDAR sensing, and energy-efficient LiDAR scanning. As a proof of concept, we mount the sensor on a ground robot platform, showing how to reduce robot motion to obtain a desired scanning resolution. We also present a ROS wrapper for active simulation for our novel sensor in Gazebo. Finally, we provide extensive empirical analysis of all our algorithms, demonstrating trade-offs between time, resolution and stand-off distance. <\/jats:p>","DOI":"10.1177\/0278364920920931","type":"journal-article","created":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T13:42:55Z","timestamp":1590068575000},"page":"837-855","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["Adaptive fovea for scanning depth sensors"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6515-4172","authenticated-orcid":false,"given":"Zaid","family":"Tasneem","sequence":"first","affiliation":[{"name":"FOCUS Lab, University of Florida, Gainesville, FL, USA"}]},{"given":"Charuvahan","family":"Adhivarahan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA"}]},{"given":"Dingkang","family":"Wang","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA"}]},{"given":"Huikai","family":"Xie","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA"}]},{"given":"Karthik","family":"Dantu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA"}]},{"given":"Sanjeev J","family":"Koppal","sequence":"additional","affiliation":[{"name":"FOCUS Lab, University of Florida, Gainesville, FL, USA"}]}],"member":"179","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"bibr1-0278364920920931","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073686"},{"key":"bibr2-0278364920920931","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073687"},{"key":"bibr3-0278364920920931","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2006.1678144"},{"key":"bibr4-0278364920920931","doi-asserted-by":"publisher","DOI":"10.1109\/5.5968"},{"key":"bibr5-0278364920920931","doi-asserted-by":"publisher","DOI":"10.1007\/BF00133571"},{"key":"bibr6-0278364920920931","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.aa.31.090193.000305"},{"key":"bibr7-0278364920920931","unstructured":"Beraldin JA, Blais F, Cournoyer L, Godin G, Rioux M, Taylor J (2003) Active 3D sensing. 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