TY - GEN
T1 - Ego-Motion Estimate Corruption Due to Violations of the Range Flow Constraint
AU - Monaco, Chris D.
AU - Brennan, Sean N.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - Visual odometry methods are increasingly being used to estimate a vehicle's ego-motion from range data due to the decreasing cost of range sensors and the impressive speed and accuracy of visual odometry techniques. Dense geometry-based visual odometry methods are fundamentally based on the range flow constraint equation, an equation which depends on the temporal and spatial derivatives of range images. However, these derivatives are calculated with the fundamental assumption that the range flow is magnitude-limited. When scaling this method for faster vehicles, this assumption could be violated, invaliding the range flow constraint equation and thus corrupting the resulting ego-motion estimates. This paper derives the sensor, motion, environment, and sampling frequency conditions that would mathematically violate the range flow constraint. This information is useful for defining the operational limits of dense geometry-based visual odometry methods.
AB - Visual odometry methods are increasingly being used to estimate a vehicle's ego-motion from range data due to the decreasing cost of range sensors and the impressive speed and accuracy of visual odometry techniques. Dense geometry-based visual odometry methods are fundamentally based on the range flow constraint equation, an equation which depends on the temporal and spatial derivatives of range images. However, these derivatives are calculated with the fundamental assumption that the range flow is magnitude-limited. When scaling this method for faster vehicles, this assumption could be violated, invaliding the range flow constraint equation and thus corrupting the resulting ego-motion estimates. This paper derives the sensor, motion, environment, and sampling frequency conditions that would mathematically violate the range flow constraint. This information is useful for defining the operational limits of dense geometry-based visual odometry methods.
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U2 - 10.1109/IROS.2018.8594131
DO - 10.1109/IROS.2018.8594131
M3 - Conference contribution
AN - SCOPUS:85062984455
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3964
EP - 3969
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Y2 - 1 October 2018 through 5 October 2018
ER -