The first biggest problem in visual motion estimation is data association; matched points contain many outliers that must be detected and removed for the motion to be accurately estimated. In the last few years, a very established method for removing outliers has been the "5-point RANSAC" algorithm which needs a minimum of 5 point correspondences to estimate the model hypotheses. Because of this, however, it can require up to thousand iterations to find a set of points free of outliers. In this talk, I will show that by exploiting the non-holonomic constraints of wheeled vehicles (e.g. cars, bikes, mobile robots) it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 point correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithm for removing outliers: 1-point RANSAC. The second problem in monocular visual odometry is the estimation of the absolute scale. I will show that vehicle non-holonomic constraints make it also possible to estimate the absolute scale completely automatically whenever the vehicle turns. In this talk, I will give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 25 Km path.
@InProceedings{scaramuzza_et_al:DagSemProc.10371.3, author = {Scaramuzza, Davide and Fraundorfer, Friedrich and Siegwart, Roland}, title = {{Real-Time Monocular Visual Odometry for On-Road Vehicles with 1-Point RANSAC}}, booktitle = {Dynamic Maps}, series = {Dagstuhl Seminar Proceedings (DagSemProc)}, ISSN = {1862-4405}, year = {2011}, volume = {10371}, editor = {Claus Brenner and Wolfram Burgard and Marc Pollefeys and Christoph Stiller}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10371.3}, URN = {urn:nbn:de:0030-drops-29500}, doi = {10.4230/DagSemProc.10371.3}, annote = {Keywords: Structure from motion, visual odometry, SLAM, RANSAC, motion constraints} }
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