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.
@cite_0: The problem of development a sustainable efficient and accurate augmented reality system possessing the interactivity property and basically implementing markerless approach for supplementing the real scene is addressed in this paper. Custom implementation is based on applying of feature-based SLAM algorithm, which is resistant to change light conditions, able to track both textured and non-textured objects in real-time mode achieved by introducing a once-performed offline system teaching step. The proposed solution is universal because of the system’s configurability in accordance with the desirable result.
@cite_1: We propose the combination of a keyframe-based monocular SLAM system and a global localization method. The SLAM system runs locally on a camera-equipped mobile client and provides continuous, relative 6DoF pose estimation as well as keyframe images with computed camera locations. As the local map expands, a server process localizes the keyframes with a pre-made, globally-registered map and returns the global registration correction to the mobile client. The localization result is updated each time a keyframe is added, and observations of global anchor points are added to the client-side bundle adjustment process to further refine the SLAM map registration and limit drift. The end result is a 6DoF tracking and mapping system which provides globally registered tracking in real-time on a mobile device, overcomes the difficulties of localization with a narrow field-of-view mobile phone camera, and is not limited to tracking only in areas covered by the offline reconstruction.
@cite_2: Extended Kalman filter (EKF) has been a popular choice to solve simultaneous localization and mapping (SLAM) problems for mobile robots or vehicles. However, the performance of the EKF depends on the correct a priori knowledge of process and sensor measurement noise covariance matrices (Q and R, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. The present paper proposes the development of a new neurofuzzy based adaptive Kalman filtering algorithm for simultaneous localization and mapping of mobile robots or vehicles, which attempts to estimate the elements of the R matrix of the EKF algorithm, at each sampling instant when a ldquomeasurement updaterdquo step is carried out. The neuro-fuzzy based supervision for the EKF algorithm is carried out with the aim of reducing the mismatch between the theoretical and the actual covariance of the innovation sequences. The free parameters of the neuro-fuzzy system are learned offline, by employing particle swarm optimization in the training phase, which configures the training problem as a high-dimensional stochastic optimization problem. By employing a mobile robot to localize and simultaneously acquire the map of the environment, under several benchmark environment situations with varying landmarks and under several conditions of wrong knowledge of sensor statistics, the performance of the proposed scheme has been evaluated. It has been successfully demonstrated that in each case, the neuro-fuzzy assistance is able to improve highly unpredictable, degrading performance of the EKF and can provide robust and accurate solutions.
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: