In this paper, we propose an novel implementation of a simultaneous localization and mapping (SLAM) system based on a monocular camera from an unmanned aerial vehicle (UAV) using Depth prediction performed with Capsule Networks (CapsNet), which possess improvements over the drawbacks of the more widely-used Convolutional Neural Networks (CNN). An Extended Kalman Filter will assist in estimating the position of the UAV so that we are able to update the belief for the environment. Results will be evaluated on a benchmark dataset to portray the accuracy of our intended approach.
Previous work include those of Chatterjee, A. @cite_2 for using a Neuro-Fuzzy EKF-based approach for SLAM problems, and Ventura, J. @cite_1 for performing localization using mobile phone. Most of these projects were done mostly with a keyframe-based approach, where if a good set of points in the image stream is detected to be tracked in the next frame, those points are designated as . A good keyframe algorithm used in earlier works is called the Harris Corner Detector @cite_0 . The basic idea is to come up with an image region which could be used for tracking if there are shifts in all directions. Mathematically this could be formulated by recording any change in approaches when shifting the window by (x,y) large for every (x,y) on the unit circle. This change is a weighted sum of square differences and denoted by S(x,y). When used with Faussian kernel with fixed variance @math , it could be written as:
@cite_0 @cite_1 @cite_2
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In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors. We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of extended Kalman filter (EKF)-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the multi-state-constraint Kalman filter (MSCKF)) attains better accuracy, consistency, and computational efficiency than the simultaneous localization and mapping (SLAM) formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF's linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-inertial measurement unit (IMU) calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte Carlo simulations and real-world testing. Reliable data association is crucial to localization and map building for mobile robot applications. For that reason, many mobile robots tend to choose vision-based SLAM solutions. In this paper, a SLAM scheme based on visual object recognition, not just a scene matching, in home environment is proposed without using artificial landmarks. For the object-based SLAM, the following algorithms are suggested: 1) a novel local invariant feature extraction by combining advantages of multi-scale Harris corner as a detector and its SIFT descriptor for natural object recognition, 2) the RANSAC clustering for robust object recognition in the presence of outliers and 3) calculating accurate metric information for SLAM update. The proposed algorithms increase robustness by correct data association and accurate observation. Moreover, it also can be easily implemented real-time by reducing the number of representative landmarks, i.e. objects. The performance of the proposed algorithm was verified by experiments using EKF-SLAM with a stereo camera in home-like environments, and it showed that the final pose error was bounded after battery-run-out autonomous navigation for 50 minutes Tracking-by-synthesis is a promising method for markerless vision-based camera tracking, particularly suitable for Augmented Reality applications. In particular, it is drift-free, viewpoint invariant and easy-to-combine with physical sensors such as GPS and inertial sensors. While edge features have been used succesfully within the tracking-by-synthesis framework, point features have, to our knowledge, still never been used. We believe that this is due to the fact that real-time corner detectors are generally weakly repeatable between a camera image and a rendered texture. In this paper, we compare the repeatability of commonly used FAST, Harris and SURF interest point detectors across view synthesis. We show that adding depth blur to the rendered texture can drastically improve the repeatability of FAST and Harris corner detectors (up to 100 in our experiments), which can be very helpful, e.g., to make tracking-by-synthesis running on mobile phones. We propose a method for simulating depth blur on the rendered images using a pre-calibrated depth response curve. In order to fulfil the performance requirements, a pyramidal approach is used based on the well-known MIP mapping technique. We also propose an original method for calibrating the depth response curve, which is suitable for any kind of focus lenses and comes for free in terms of programming effort, once the tracking-by-synthesis algorithm has been implemented. The main contribution of this paper is an extended Kalman filter (EKF)based algorithm for estimating the 6 DOF pose of a camera using monocular images of an indoor environment. In contrast to popular visual simultaneous localisation and mapping algorithms, the technique proposed relies on a pre-built map represented as an unsigned distance function of the ground plane edges. Images from the camera are processed using a Convolutional Neural Network (CNN)to extract a ground plane edge image. Pixels that belong to these edges are used in the observation equation of the EKF to estimate the camera location. Use of the CNN makes it possible to extract ground plane edges under significant changes to scene illumination. The EKF framework lends itself to use of a suitable motion model, fusing information from any other sensors such as wheel encoders or inertial measurement units, if available, and rejecting spurious observations. A series of experiments are presented to demonstrate the effectiveness of the proposed technique.