Abstract
Kanade-Lucas-Tomasi (KLT) optical flow algorithm based on the brightness constancy assumption is widely used in visual simultaneous localization and mapping (SLAM) and visual odometry (VO). However, the automatic adjustment of camera exposure time, the attenuation factor of sensor irradiance caused by vignetting, and the nonlinear camera response function will cause the same feature point to have different brightness values on different image frames, thus breaking this assumption. Hence, we propose a gain-adaptive KLT optical flow algorithm with online photometric calibration, and on this basis, design a monocular visual-inertial odometry which is insensitive to brightness changes. This method can calibrate the photometric parameters online in real time, meet the assumption of constant brightness in practical applications, and make the algorithm more robust and accurate in the case of dynamic changes in brightness. Experimental results on the TUM Mono and EuRoC datasets show that the proposed algorithm can reliably calibrate the photometric parameters of any video sequence and perform well in the environment with varying brightness.
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