Abstract-We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle stateestimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software.
FAST has been recently utilized in the semi-direct monocular visual odometry REF in which FAST features are tracked and used to build a map that is further used for a visual odometry estimation.
SVO: Fast semi-direct monocular visual odometry
2014 IEEE International Conference on Robotics and Automation (ICRA)
2014 IEEE International Conference on Robotics and Automation (ICRA)
Engineering Computer Science