@conference {PIZARRO:2007:INRIA-00198431:1,
	title = {Mobile Robot Geometry Initialization from Single Camera},
	booktitle = {6th International Conference on Field and Service Robotics - FSR 2007 Field and Service Robotics Springer Tracts in Advanced Robotics},
	series = {{S}pringer {T}racts in {A}dvanced {R}obotics},
	volume = {42},
	year = {2007},
	note = {<p>{I}.: {C}omputing {M}ethodologies/{I}.2: {ARTIFICIAL} {INTELLIGENCE}/{I}.2.9: {R}obotics</p>},
	month = {12/2007},
	pages = {10},
	publisher = {Springer},
	organization = {Springer},
	address = {Chamonix France},
	abstract = {<p>{U}sing external cameras to achieve robot localization has been widely proposed in the area of {I}ntelligent {S}paces. {R}ecently, an online approach that simultaneously obtains robot\&rsquo;s pose and its 3{D} structure using a single external camera has been developed [8]. {S}uch proposal relies on a proper initialization of pose and structure information of the robot. {T}he present paper proposes a solution to initialization which consists of retrieving 3{D} structure and motion of a rigid object from a set of point matches measured by the camera. {A} batch {S}tructure from {M}otion ({SFM}) approach is proposed along a short path. {B}y incorporating odometry information available in the robot, the ambiguity generated by a single view in the solution is solved. {W}e propose to describe robot\&rsquo;s motion and image detection as statistical processes in which the uncertainty is properly modelled. {U}sing a {G}aussian equivalence of the processes involved, the {SFM} cost function is expressed as a {M}aximum {L}ikelihood optimization. {T}he paper shows the improvements of the approach in the presence of the usual odometry drift noise, compared with those using {E}uclidean distance as a likelihood. {T}he proposed method is assessed on synthetic and real data.</p>},
	url = {http://hal.inria.fr/inria-00198431/en/},
	author = {Daniel Pizarro and Manuel Mazo and Enrique Santiso and Hashimoto, Hideki}
}
