Background
Visual odometry (VO) is a process used in robotics and autonomous vehicles to estimate the position and orientation (i.e. pose) of a moving camera or robot by analyzing sequences of images. Visual odometry is particularly useful in environments where other localization methods, such as GPS, may not be available or reliable.
Challenges
Real-Time Processing. VO algorithms need to run in real-time, which can be computationally intensive, especially for high-resolution images and complex environments.
High Speed Camera Motion. Rapid camera movements can cause motion blur, making it difficult to detect and track features accurately.
Low Frame Rate Camera Motion. If the camera captures images too slowly, the motion between frames can be too large, leading to poor feature matching.
Rotational Motion Camera Motion. Pure rotational motion can make it challenging to estimate translation, as there is no parallax effect.
Monocular Camera Systems. Monocular cameras cannot directly measure depth, leading to scale ambiguity. The same motion can appear different at different scales, making it hard to estimate the absolute scale of the environment .
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