Comparative study of SURF, KAZE and MSER for visual odometry in presence of noise

Pose estimation is a key aspect of mobile robots or moving vehicles in computer vision and robotics applications when it travels through an environment. Monocular visual odometry can provide improved pose estimation compared to wheel odometry method when travelling on slippery or smooth surface. Method of estimating trajectory of camera mounted on vehicle using monocular visual odometry. Fast and robust image feature detection and matching techniques are very important for visual odometry. To test the effect of salt and pepper noise on visual odometry, we added noise to KITTI dataset. There is need to investigate the performances of SURF (Speeded-Up Robust Features), KAZE and MSER (Maximally Stable Extremal Regions) in presence of salt and pepper noise to obtain good feature detection and matching because good feature matching provides good visual odometry. Salt and pepper noise can affect the accuracy of visual odometry so there is need to study and find the robust feature detector in presence of noise to obtain good camera trajectory. There is need to investigate and compare the performances of SURF (Speeded-Up Robust Features), KAZE and MSER (Maximally Stable Extremal Regions) in presence of salt and pepper noise to obtain good camera trajectory. By mixing noise with KITTI dataset images and matching rate is identified and robust feature detector is one which provides good feature matching rate. Trajectories are plotted by mixing noise 5% with images of KITTI dataset and tested the effect of salt and pepper noise on trajectories. Plotted trajectories are compared with ground truth and RMSE is calculated. Visual odometry trajectories are plotted using SURF, KAZE and MSER and these trajectories are compared with ground truth. Experiments performed using KITTI dataset have shown that visual odometry obtained using KAZE is better than SURF and MSER in presence noise but requires more time. Root means square error (RMSE) for KAZE is less than SURF and MSER.