This technology is a vision-based odometry system and method that minimizes photometric errors in environments with significant lighting fluctuations. It establishes an affine illumination change model between keyframes and the current frame, and performs illumination correction using planar patch-based residual vectors and Jacobian matrices.
Conventional vision-based odometry assumes constant lighting conditions, which leads to drift in estimated trajectories when sudden illumination changes occur due to camera auto-exposure, shadows, or moving clouds.
This technology proposes a method that selects planar patches using a blob detector and RANSAC, and applies an affine illumination change model to calibrate inter-frame lighting parameters. By using the ESM algorithm to minimize the sum of squared photometric errors, it enables robust camera pose estimation even under irregular lighting changes. It secures the reliability of position estimation in environments with severe lighting variations, such as outdoor autonomous driving, drone navigation, and outdoor robotics, significantly expanding the practical application range of vision-based navigation.
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