scholarly journals Homography Based Egomotion Estimation with a Common Direction

2017 ◽  
Vol 39 (2) ◽  
pp. 327-341 ◽  
Author(s):  
Olivier Saurer ◽  
Pascal Vasseur ◽  
Remi Boutteau ◽  
Cedric Demonceaux ◽  
Marc Pollefeys ◽  
...  
Keyword(s):  
Egomotion Estimation

scholarly journals Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation

2014 ◽  
Vol II-2 ◽  
pp. 61-66 ◽  
Author(s):  
N. S. Gopaul ◽  
J. G. Wang ◽  
B. Hu
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Process Noise ◽  
Shaping Filter ◽  
Egomotion Estimation ◽  
Simulation Results ◽  
The One ◽  
Better Than

An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are <i>pairwise</i> correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch <i>k</i> is not only correlated with the one from epoch <i>k</i> &ndash; 1 but also with the ones before epoch <i>k</i> &ndash; 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.

Improving robustness for real-time vehicle egomotion estimation

2016 ◽  
Author(s):  
Stephanie Lessmann ◽  
Jan Siegemund ◽  
Mirko Meuter ◽  
Jens Westerhoff ◽  
Josef Pauli
Keyword(s):  
Real Time ◽  
Egomotion Estimation

High Frame Rate Egomotion Estimation

2013 ◽  
pp. 183-192 ◽  
Author(s):  
Natesh Srinivasan ◽  
Richard Roberts ◽  
Frank Dellaert
Keyword(s):  
Frame Rate ◽  
High Frame Rate ◽  
Egomotion Estimation
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