Globally Consistent Range Scan Alignment for Environment Mapping

Lu, F; Milios, E

dc.contributor.author	Lu, F	en
dc.contributor.author	Milios, E	en
dc.date.accessioned	2014-03-01T01:45:56Z
dc.date.available	2014-03-01T01:45:56Z
dc.date.issued	1997	en
dc.identifier.issn	09295593	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/24803
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0031249780&partnerID=40&md5=4a99b1460267d3114f041c16760a2ca9	en
dc.subject	Laser range scanning	en
dc.subject	Mapping	en
dc.subject	Range scan alignment	en
dc.subject	Range scan registration	en
dc.subject	Sensor-based mobile robotics	en
dc.subject.other	Computer simulation	en
dc.subject.other	Data processing	en
dc.subject.other	Distance measurement	en
dc.subject.other	Mapping	en
dc.subject.other	Mathematical models	en
dc.subject.other	Navigation	en
dc.subject.other	Problem solving	en
dc.subject.other	Scanning	en
dc.subject.other	Sensors	en
dc.subject.other	Maximum likelihood criterion	en
dc.subject.other	Odometry	en
dc.subject.other	Range scan alignment	en
dc.subject.other	Range scan registration	en
dc.subject.other	World model	en
dc.subject.other	Mobile robots	en
dc.title	Globally Consistent Range Scan Alignment for Environment Mapping	en
heal.type	journalArticle	en
heal.publicationDate	1997	en
heal.abstract	A robot exploring an unknown environment may need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is aligned to a cumulative global model, and then merged to the model. Because different parts of the model are updated independently while there are errors in the registration, such an approach may result in an inconsistent model. In this paper, we study the problem of consistent registration of multiple frames of measurements (range scans), together with the related issues of representation and manipulation of spatial uncertainties. Our approach is to maintain all the local frames of data as well as the relative spatial relationships between local frames. These spatial relationships are modeled as random variables and are derived from matching pairwise scans or from odometry. Then we formulate a procedure based on the maximum likelihood criterion to optimally combine all the spatial relations. Consistency is achieved by using all the spatial relations as constraints to solve for the data frame poses simultaneously. Experiments with both simulated and real data will be presented.	en
heal.journalName	Autonomous Robots	en
dc.identifier.volume	4	en
dc.identifier.issue	4	en
dc.identifier.spage	333	en
dc.identifier.epage	349	en