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In this work, we use computer vision to improve odometry result of multiple robots with shared information amongst neighboring region. We use a stereo camera to captures images. Each robot handles visual odometry to localize itself using stereo images. For every frame that is captured, there are errors in calculations, and these errors do accumulate and cause drift in odometry results. To solve this...
AGV path planning problems play an extremely important role in navigations of AGV. Intelligence algorithms provide an effective way to solve such complicated problems. Artificial fish swarm algorithm (AFSA) is a newly proposed promising swarm intelligence optimization algorithm, yet there still exist some disadvantages of it, such as low optimization precision and convergence rate. Aiming at these...
A novel scheme with deep cross-modal correlation learning is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR) for large-scale annotated images. It integrates the deep multimodal feature generation, deep cross-modal correlation learning and similarity search optimization through mining all the beneficial multimodal information sources in sketches and images,...
Recent work in computer graphics has explored the synthesis of indoor spaces with furniture, accessories, and other layout items. In this work, we bridge the gap between the physical and virtual worlds: Given an input image of an interior or exterior space, and a general user specification of the desired furnishings and layout constraints, our method automatically furnishes the scene with a realistic...
We consider the problem of row-column sparse linear quadratic controller (LQC) design. An optimization problem is formulated in which the quadratic performance loss is minimized subject to satisfaction of m+n sparsity constraints to obtain the row-column (r, c)-sparse LQC design where m and n refer to the number of inputs and states, respectively and r/c represent the maximum allowed density level...
Machine-to-machine (M2M) communication is one of the latest technologies to support connectivity among numerous intelligent devices. The intelligence of M2M systems can be enhanced by incorporating visual sensor networks (VSNs) and utilising visual information. The conservation of energy within VSNs is one of the primary concerns for resource constrained scenarios, which can be achieved from targeted...
This paper presents a fast RGB-D dense visual odometry estimating 12-DoF state information including 3D motion and 6-DoF spatial velocity of a camera-strapdown system. To reduce computational loads, we extract informative pixels through a zero-crossing difference of Gaussian (DoG) and non-maximum gradient pixel extraction. For extracted regions, the 3D motion is estimated through inverse compositional...
The one of the most vital public transport problems in cities is discrepancy between transport systems and numerous requirements to urban mobility. One of the crucial tasks is optimization of bus stops location for purposes of public transport usability and convenience. Most of current methods of solving such problems omit various obstacles and models of pedestrian motion. The study proposes a new...
The aim of Two-layer network visualization is to help users explore in the networks from the view of application requirement. The research abstracts applications and networks that carry them as business layer and carrier layer, which represent separately all kinds of business and service relationships among application entities and the path of information transmission in network world. A two-layer...
In many-objective optimization, visualization of population in the high-dimensional objective space provides a critical understanding of the Pareto front. First, visualization throughout the evolutionary process can be exploited in developing effective many-objective evolutionary algorithms. Furthermore, visualization is a crucial component of multi-criteria decision making. By directly observing...
In many-objective optimization, the dimensionality of Pareto fronts becomes higher than three, and extracting preferable points for the decision maker is a key issue in the post-optimal analysis. The aim of this study is to develop a method to detect and visualize high-dimensional knee points. We propose a new definition of knee point and a graph-based approach to detect our knee points with a visualization...
Multimodal optimization has attracted increasing interest recently. Despite the emergence of various multimodal optimization algorithms during the last decade, little work has been dedicated to the development of benchmark tools. In this paper, we propose a visualization method for benchmark studies of multimodal optimization, called parallel peaks. Inspired by parallel coordinates, the proposed parallel...
Multi-target stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, a large interval between two neighboring stimulus frequencies is often used to improve classification accuracy. Although recent progresses in stimulus coding and target identification methods that have significantly improved...
Real-time monocular SLAM is increasingly mature and entering commercial products. However, there is a divide between two techniques providing similar performance. Despite the rise of ‘dense’ and ‘semi-dense’ methods which use large proportions of the pixels in a video stream to estimate motion and structure via alternating estimation, they have not eradicated feature-based methods which use a significantly...
Direct visual odometry and Simultaneous Localization and Mapping (SLAM) methods determine camera poses by means of direct image alignment. This optimizes a photometric cost term based on the Lucas-Kanade method. Many recent works use the brightness constancy assumption in the alignment cost formulation and therefore cannot cope with significant illumination changes. Such changes are especially likely...
In this work, we present a solution to real-time monocular dense mapping. A tightly-coupled visual-inertial localization module is designed to provide metric and high-accuracy odometry. A motion stereo algorithm is proposed to take the video input from one camera to produce local depth measurements with semi-global regularization. The local measurements are then integrated into a global map for noise...
Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational power, or insufficient memory for storing large maps of the entire environment. Thus, developing compact...
Conventional camera calibration techniques rely on discrete reference points extracted from a set of input images. While these approaches have been applied successfully for a long time, omitting all image information apart from reference point positions at the initial stage of the calibration pipeline renders correct treatment of uncertainties difficult and gives rise to complications in timestamping...
We present a data processing pipeline to online estimate ego-motion and build a map of the traversed environment, leveraging data from a 3D laser, a camera, and an IMU. Different from traditional methods that use a Kalman filter or factor-graph optimization, the proposed method employs a sequential, multi-layer processing pipeline, solving for motion from coarse to fine. The resulting system enables...
Recent advancements in the performance and affordability of cameras and inertial measurement units (IMUs) have caused demand for efficient, accurate visual-inertial navigation solutions. In this paper, we present a system for the fusion of preintegrated inertial measurements with highly informative direct alignment of images. In particular, our preintegration theory is based on closed-form solutions...
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