Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports
Journal of Field Robotics - Special Issue on Space Robotics
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-based road detection in automotive systems: a real-time expectation-driven approach
Journal of Artificial Intelligence Research
Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles
IEEE Transactions on Robotics
Color-based road detection in urban traffic scenes
IEEE Transactions on Intelligent Transportation Systems
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
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When a robot travels in urban area, Global Positional System (GPS) signals might be obstructed by buildings. Hence visual odometry is a choice. We notice that the vertical edges from high buildings and poles of street lights are a very stable set of features that can be easily extracted. Thus, we develop a monocular vision-based odometry system that utilizes the vertical edges from the scene to estimate the robot ego-motion. Since it only takes a single vertical line pair to estimate the robot ego-motion on the road plane, here we model the ego-motion estimation process and analyze how the choice of different vertical line pair impacts the accuracy of the ego-motion estimation process. The resulting closed form error model can assist to choose an appropriate pair of vertical lines to reduce the error in computation. We have implemented the proposed method and validated the error analysis results in physical experiments.