text: In this paper we present an on-manifold sequence-to-sequence learning approach to motion estimation using visual and inertial sensors. It is to the best of our knowledge the first end-to-end trainable method for visual-inertial odometry which performs fusion of the data at an intermediate feature-representation level. Our method has numerous advantages over traditional approaches. Specifically, it eliminates the need for tedious manual synchronization of the camera and IMU as well as eliminating the need for manual calibration between the IMU and camera. A further advantage is that our model naturally and elegantly incorporates domain specific information which significantly mitigates drift. We show that our approach is competitive with state-of-the-art traditional methods when accurate calibration data is available and can be trained to outperform them in the presence of calibration and synchronization errors.
  @cite_11: 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 state-estimation 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.
summary: VO algorithms estimate the incremental ego-motion of a camera. A traditional VO algorithm, illustrated in Fig. a., operates by extracting features in an image, matching the features between the current and successive images and then computing the optical flow. The motion can then be computed using the optical flow. The fast semi-direct monocular visual odometry (SVO) algorithm @cite_11 is an example of a state-of-the-art VO algorithm. It is designed to be fast and robust by operating directly on the image patches, not relying on slow feature extraction. Instead, it uses probabilistic depth filters on patches of the image itself. The depth filters are then updated through whole image alignment. This visual odometry algorithm is efficient and runs in real-time on an embedded platform. Its probabilistic formulation, however, makes it difficult to tune and it also requires a bootstrapping procedure to start the process. As expected, its performance depends heavily on the hardware to prevent tracking failures - typically global shutter cameras operating at higher than 50 fps needs to be used to ensure the odometry estimates remain accurate.