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This example code implements an algorithm that consists of 1) detect 5000 fast key points, 2) calculate optical flow to get matched key points, 3) find essential matrix, and 4) find pose. The translation vector is scaled to match the actual displacement between the photos. The program is set up to read photo files from the KITTI Odometry database and compare the calculate trajectory with the measured trajectory.

The program can be easily translated to Java, and modified to 1) orb detect, 2) brisk extract / brute force haminglut, but the results do not match as well using 500 orb key points as the 5000 fast key

This example code implements an algorithm that consists of 1) detect 5000 fast key points, 2) calculate optical flow to get matched key points, 3) find essential matrix, and 4) find pose. The translation vector is scaled to match the actual displacement between the photos. The program is set up to read photo files from the KITTI Odometry database and compare the calculate trajectory with the measured trajectory.

The program can be easily translated to Java, and modified to 1) orb detect, 2) brisk extract / brute force haminglut, but the results do not match as well using 500 orb key points as the 5000 fast key

This example code implements an algorithm that consists of 1) detect 5000 fast key points, 2) calculate optical flow to get matched key points, 3) find essential matrix, and 4) find pose. The translation vector is scaled to match the actual displacement between the photos. The program is set up to read photo files from the KITTI Odometry database and compare the calculate calculated trajectory with the measured trajectory.

The program can be easily translated to Java, and modified to 1) orb detect, 2) brisk extract / brute force haminglut, but the results do not match as well using 500 orb key points as the 5000 fast key

This example code implements an algorithm that consists of 1) detect 5000 fast key points, 2) calculate optical flow to get matched key points, 3) find essential matrix, and 4) find pose. The translation vector is scaled to match the actual displacement between the photos. The program is set up to read photo files from the KITTI Odometry database and compare the calculated trajectory with the measured trajectory.

The program can be easily translated to Java, and modified to 1) orb detect, 2) brisk extract / brute force haminglut, haminglut match, but the results do not match as well using 500 orb key points as the 5000 fast key points with optical flow matching.

This One approach is this example code implements an algorithm that consists of 1) detect 5000 fast key points, 2) calculate optical flow to get matched key points, 3) find essential matrix, and 4) find pose. The To solve the problem of scaling, the translation vector is scaled to match the actual displacement between the photos. The program is set up to read photo files from the KITTI Odometry database and compare the calculated trajectory with the measured trajectory.

The program can be easily translated to Java, and modified to 1) orb detect, 2) brisk extract / brute force haminglut match, but the results do not match as well using 500 orb key points as the 5000 fast key points with optical flow matching.

One approach is this example code implements an algorithm that consists of 1) detect 5000 fast key points, 2) calculate optical flow to get matched key points, 3) find essential matrix, and 4) find pose. To solve the problem of scaling, the translation vector is scaled to match the actual displacement between the photos. The program is set up to read photo files from the KITTI Odometry database and compare the calculated trajectory with the measured trajectory.

The program can be easily translated to Java, and modified to 1) orb detect, 2) brisk extract / brute force haminglut match, but the results do not match as well using 500 orb key points as the 5000 fast key points with optical flow