<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Andreas Neufeld</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Constant Acceleration Model</style></keyword><keyword><style  face="normal" font="default" size="100%">Lie Group</style></keyword><keyword><style  face="normal" font="default" size="100%">Minimum Energy Filter</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal Control</style></keyword><keyword><style  face="normal" font="default" size="100%">Recursive Filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Odometry</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1507.06810</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Camera motion estimation from observed scene features is an important task in image processing to increase the accuracy of many methods, e.g. optical flow and structure-from-motion. Due to the curved geometry of the state space SE(3) and the non-linear relation to the observed optical flow, many recent filtering approaches use a first-order approximation and assume a Gaussian a posteriori distribution or restrict the state to Euclidean geometry. The physical model is usually also limited to uniform motions. 
We propose a second-order minimum energy filter with a generalized kinematic model that copes with the full geometry of SE(3) as well as with the nonlinear dependencies between the state space and observations. The derived filter enables reconstructing motions correctly for synthetic and real scenes, e.g. from the KITTI benchmark. Our experiments confirm that the derived minimum energy filter with higher-order state differential equation copes with higher-order kinematics and is also able to minimize model noise. We also show that the proposed filter is superior to state-of-the-art extended Kalman filters on Lie groups in the case of linear observations and that our method reaches the accuracy of modern visual odometry methods.</style></abstract><custom2><style face="normal" font="default" size="100%">arXiv preprint</style></custom2></record></records></xml>