Walsh Hadamard Transform (WHT) is an orthogonal, symmetric, involutional, and linear operation used in data encryption, data compression, and quantum computing. The WHT belongs to a generalized class of Fourier transforms, which allows that many algorithms developed for the fast Fourier transform (FFT) work for fast WHT implementations (FWHT). This paper employs this property and uses a parallel-pipeline FFT well-known strategy for VLSI implementation to build parallel-pipeline architectures for FWHT. We apply the FFT parallel-pipeline approach on a Fast WHT and use the High-Level Synthesis (HLS) tool from Xilinx Vitis to generate an FPGA solution. We also provide an open-source code with the basic blocks to build any model with any parallelization level. The parallel-pipeline proposed solutions achieve a latency reduction of up to 3.57% compared to a pipeline approach on a 256-long signal using 32 bit floating-point numbers.

Quadcopters can be used in search, rescue and surveillance operations, but under these conditions, they experience aerodynamic disturbances caused by the surfaces that are close to them. In this work, we propose a disturbance rejection strategy for the outer loop control of a quadrotor in the presence of ground effect. The strategy is based on the rapid switching of control algorithms with a novel switching method. We prove that under our approach, it is possible to improve the flight performance near ground compared to standard methods that use conventional position feedback.

Nowadays urban structures (lines, planes, spheres, etc.) recognition is a useful task under computer vision systems since it provides rich scene information that can be exploited to understand the scene. In this context, one popular trend is for urban planar structures recognition (floor/ground recognition), because they have consistent appearance under urban scenarios and they can be used to improve several computer vision applications performance, for example, in autonomous vehicle navigation, robotic control, 3D modeling, etc. In the current literature, there are several approaches for floor recognition. However, most previous work has low robustness under image degradations (blur, lighting changes, noise, etc.). One alternative to address the image degradation problems is the use of binary features (for example LBP features). In this work, we propose a new binary-patterns based floor recognition suitable for urban scenes. For that, we propose two analyses, first we consider the floor connection to increase the recognition and second we segment the recognition in floor surface sets to remove the floor misrecognition. Finally, experimental results demonstrated that the proposed method delivers high stability under different scenes and it has more recognition than previous work under floor recognition domain.

Recognition of dominant planes is an important task used in areas such as robot navigation, augmented reality, 3D reconstruction, among others. There are several approaches for recognizing planar structures, however, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds associated with the camera images. An alternative is to process a single image seeking to interpret areas of the images where the planar structure may be observed, thus removing parallax dependency, but adding the challenge of having to correctly interpret image ambiguities. Motivated by the latter, this work presents initial results of a novel methodology for dominant planes recognition in a single image by combining three key strategies: a learning algorithm, a segmentation scheme and a contour detection method. We constraint our approach to work with interior scenes as an attempt to identify key elements that may help in the recognition process in this sort of scenes. In this sense, our results show a recognition accuracy of 60.17% with an error of 3.14%, which indicate the feasibility of our approach.

The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains. In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm.

We investigate a new approach to vision based mapping, in which single image structure recognition is used to derive strong priors for initialisation of higher-level primitives in the map. This can reduce state size and speed up the building of more meaningful maps. We focus on plane mapping and use a recognition algorithm to detect and estimate the 3D orientation of planar structures in key frames, which are then used as priors for initialising planes in the map. The recognition algorithm learns the relationship between such structure and appearance from training examples offline. We demonstrate the approach in the context of an EKF based visual odometry system. Preliminary results of experiments in urban environments show that the system is able to build large maps with significant planar structure at average frames rates of around 60 fps whilst maintaining good trajectory estimation. The results suggest that the approach has considerable potential.

Relocalisation in 6D is relevant to a variety of Robotics applications and in particular to agile cameras exploring a 3D environment. While the use of geometry has commonly helped to validate appearance as a back-end process in several relocalisation systems before, we are interested in using 3D information to assist fast pose relocalisation computation as part of a front-end task. Our approach rapidly searches for a reduced number of visual descriptors, previously observed and stored in a database, that can be used to effectively compute the camera pose corresponding to the current view. We guide the search by means of constructing validated candidate sets using a 3D test involving the depth information obtained with an RGB-D camera (e.g. stereo of with structured light). Our experiments demonstrate that this process returns a compact quality set that works better for the pose estimation stage than when using a typical Nearest-Neighbor search over appearance only. The improvements are observed in terms of percentage of relocalised frames and speed, where the latter goes up to two orders of magnitude w.r.t. the conventional search.

We describe a method for visual odometry using a single camera based on an EKF framework. Previous work has shown that filtering based approaches can achieve accuracy performance comparable to that of optimisation methods providing that large numbers of features are used. However, computational requirements are significantly increased and frame rates are low. We address this by employing higher level structure - in the form of planes - to efficiently parameterise features and so reduce the filter state size and computational load. Moreover, we extend a 1-point RANSAC outlier rejection method to the case of features lying on planes. Results of experiments with both simulated and real-world data demonstrate that the method is effective, achieving comparable accuracy whilst running at significantly higher frame rates.

Planar features in filter-based Visual SLAM systems require an initialisation stage that delays their use within the estimation. In this stage, surface and pose are initialised either by using an already generated map of point features [2, 3] or by using visual clues from frames [4]. This delay is unsatisfactory specially in scenarios where the camera moves rapidly such that visual features are observed for a very limited period. In this paper we present a unified approach to mapping in which points and planes are initialised alongside each other within the same framework. The best structure emerges according to what the camera observes, thus avoiding delayed initialisation for planar features. To do this we use a similar parameterisation to the one used for planar features in [3, 4]. The Inverse Depth Planar Parameterisation (IDPP), as we call it, is used to represent both planes and points. This IDPP is also combined with a point based measurement model where the planar constraint is introduced. The latter allows us to estimate and grow a planar structure if suitable, or to estimate a 3-D point if visual measurements do not support the constraint. The IDPP contains three main components: (1) A reference camera (RC); (2) the depth w.r.t. the RC of a seed 3-D point on the plane; (3) the normal of the plane.

The visual simultaneous localisation and mapping (SLAM) systems now in widespread use are based on localised point features [2, 4, 5]. Although effective in many respects, the approach has limitations when considering the density and efficiency of map representation. With a dense population of features, camera tracking can be robust, able to withstand significant occlusion and large changes in camera viewpoint. But this comes at a high computational cost, typically increasing quadratically with the number of features. In this work we propose increasing map density by building in higherorder structure in the form of planar features. An important and novel aspect of the work is the manner in which the planar features are updated and used to localise the camera. We base our approach on an extended Kalman filter (EKF) monocular SLAM system developed by Chekhlov et al. [3]. This provides real-time estimates of the 3-D pose of a calibrated camera whilst simultaneously mapping the scene in terms of point based features. In order to incorporate planar structure into the real-time monocular SLAM we carry out three steps: detection of planar structure in the scene; insertion of planar features into the map; and adaptive measurement of the features. To apply the principle of adaptive measurement it is essential that planar features inserted into the map correspond to actual planar structure in the scene. For this we employ the method proposed by Martinez-Carranza and Calway [6], which uses an appearance model to detect planes defined by subsets of mapped point features (at least three points). Having detected planar features in the scene these are inserted into the map using a suitable representation within the filter state. This has two components: plane parameterisation and the reference camera. The plane is defined by yp = (θ ,φ ,ρ), where (θ ,φ) defines the unit normal of the plane in polar coordinates in the reference camera and ρ is the inverse depth of the plane centre along the ray defined by uo, with the latter being stored at initialisation of the plane, see figure 1a. Insertion of the reference camera is done by augmenting the state with a copy of the current pose, i.e. vp = v, and with initialised plane parameters yp derived from the pose and the subset of mapped points which define the plane. The reference camera serves two purposes: it references the plane in the SLAM coordinate system (with the associated uncertainties) and enables subsequent measurement of the planar feature using region based matching with respect to the current frame (key frame). To facilitate the latter the key frame image is also stored in the system. As illustrated in figure 1b, measurements for a planar feature are therefore assumed to take the following form: