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New development in robot vision
\"The field of robotic vision has advanced dramatically recently with the development of new range sensors. Tremendous progress has been made resulting in significant impact on areas such as robotic navigation, scene/environment understanding, and visual learning. This edited book provides a solid and diversified reference source for some of the most recent important advancements in the field of robotic vision. The book starts with articles that describe new techniques to understand scenes from 2D/3D data such as estimation of planar structures, recognition of multiple objects in the scene using different kinds of features as well as their spatial and semantic relationships, generation of 3D object models, approach to recognize partially occluded objects, etc. Novel techniques are introduced to improve 3D perception accuracy with other sensors such as a gyroscope, positioning accuracy with a visual servoing based alignment strategy for microassembly, and increasing object recognition reliability using related manipulation motion models. For autonomous robot navigation, different vision-based localization and tracking strategies and algorithms are discussed. New approaches using probabilistic analysis for robot navigation, online learning of vision-based robot control, and 3D motion estimation via intensity differences from a monocular camera are described. This collection will be beneficial to graduate students, researchers, and professionals working in the area of robotic vision.\"--back cover.
Book
Visual Perception and Control of Underwater Robots
This book covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection.
With the aid of visual perception technologies, two up-to-date underwater robot systems are demonstrated. The first system focuses on underwater robotic operation for the task of object collection in sea. The other one is an untethered biomimetic robotic fish with a camera stabilizer and its control methods based on visual tracking. The authors provide a self-contained and comprehensive guide to understand underwater visual perception and control. Bridging the gap between theory and practice in underwater vision, the book features implementable algorithms, numerical examples, and tests, where codes are publicly available. Meanwhile, the mainstream technologies that are covered in the book include deep learning, adversarial learning, evolutionary computation, robust control, and underwater bionics.
Researchers, senior undergraduate and graduate students, and engineers dealing with underwater visual perception and control will benefit from the book.
eBook
Switchable constraints for robust simultaneous localization and mapping and satellite-based localization
Simultaneous Localization and Mapping (SLAM) has been a long-standing research problem in robotics. This resource describes the problem of a robot mapping an unknown environment, while simultaneously localising in it with the help of the incomplete map. This book summarises the foundations of factor graph-based SLAM techniques. It explains the problem of data association errors before introducing the novel idea of Switchable Constraints.
BOOK
Robot learning by visual observation
This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstractionDiscusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problemFocuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regressionConcentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
eBook
A target tracking and location robot system based on omnistereo vision
Purpose
Because of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.
Design/methodology/approach
The authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.
Findings
The experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.
Originality/value
A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.
Journal Article
Deep Learning-Based Cost-Effective and Responsive Robot for Autism Treatment
Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot is an excellent tool to be used in therapy and teaching. It can transform teaching methods, not just in the classrooms but also in the in-house clinical practices. With the rapid advancement in deep learning techniques, robots became more capable of handling human behaviour. In this paper, we present a cost-efficient, socially designed robot called ‘Tinku’, developed to assist in teaching special needs children. ‘Tinku’ is low cost but is full of features and has the ability to produce human-like expressions. Its design is inspired by the widely accepted animated character ‘WALL-E’. Its capabilities include offline speech processing and computer vision—we used light object detection models, such as Yolo v3-tiny and single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions in an anthropomorphic way, etc. It uses an onboard deep learning technique to localize the objects in the scene and uses the information for semantic perception. We have developed several lessons for training using these features. A sample lesson about brushing is discussed to show the robot’s capabilities. Tinku is cute, and loaded with lots of features, and the management of all the processes is mind-blowing. It is developed in the supervision of clinical experts and its condition for application is taken care of. A small survey on the appearance is also discussed. More importantly, it is tested on small children for the acceptance of the technology and compatibility in terms of voice interaction. It helps autistic kids using state-of-the-art deep learning models. Autism Spectral disorders are being increasingly identified today’s world. The studies show that children are prone to interact with technology more comfortably than a with human instructor. To fulfil this demand, we presented a cost-effective solution in the form of a robot with some common lessons for the training of an autism-affected child.
Journal Article
Construction of computer visual dataset for autonomous driving in sand‐dust weather
With the wide application of vision‐based autonomous driving and mobile robots, the impact of frequent sand‐dust weather on computer vision applications in landlocked countries during spring and autumn has also attracted more and more attention. Although there has been a lot of research on sand‐dust image enhancement, no research has been conducted on how to improve the positioning accuracy of vision‐based autonomous driving or mobile robots in sand‐dust environments, especially because there is currently a lack of dataset to evaluate visual positioning in sand‐dust weather. Therefore, a complete set of visual positioning dataset construction methods in sand‐dust weather is proposed to fill the gap in the evaluation dataset of application fields such as autonomous driving or mobile robot attitude estimation in sand‐dust weather. At the same time, this method is also suitable for the construction of visual positioning dataset under haze and other similar weather. In addition, this paper further demonstrates to readers how to use the converted dust visual positioning dataset to conduct positioning evaluation experiment of automatic driving in sand‐dust weather. A complete set of visual positioning data set construction methods are proposed in sand‐dust weather to fill the gap in the evaluation data set of application fields such as autonomous driving or mobile robot attitude estimation in sand‐dust weather. At the same time, this method is also suitable for the construction of visual positioning data sets under haze and other similar weather. In addition, this paper further demonstrates to readers how to use the converted dust visual positioning data set to conduct positioning evaluation experiment of automatic driving in sand‐dust weather.
Journal Article
Dual‐Litenet: A Lightweight Real‐Time Semantic Segmentation and Boundary Detection Method for Mobile Embodied Agents
Semantic segmentation and boundary detection serve as the foundational stages in the visual language navigation task within embodied intelligence. Nevertheless, existing semantic segmentation and boundary detection methods employed in visual language navigation fail to satisfy the demands for real‐time performance and accuracy under resource‐constrained scenarios. In response to this challenge, this study introduces a dual‐branch network. This architecture is designed to be lightweight while fulfilling the real‐time requirements of the agent and achieving an accuracy that is only marginally lower than that of non‐lightweight state‐of‐the‐art methods. Experimental results demonstrate that this approach achieves a parameter count of 2.7M, FLOPs of 29.93G, and a frame rate of 32.18 FPS. This lightweight dual‐branch network integrates a semantic branch (with lightweight ASPP and dual‐dimensional attention for multi‐scale fusion) and an edge branch (using Ghost modules and bidirectional attention for efficient extraction). A learnable edge detector with dynamic convolution kernels enhances boundary precision, enabling real‐time segmentation while balancing accuracy and efficiency.
Journal Article
Esup.2-VINS: An Event-Enhanced Visual–Inertial SLAM Scheme for Dynamic Environments
Simultaneous Localization and Mapping (SLAM) technology has garnered significant interest in the robotic vision community over the past few decades. The rapid development of SLAM technology has resulted in its widespread application across various fields, including autonomous driving, robot navigation, and virtual reality. Although SLAM, especially Visual–Inertial SLAM (VI-SLAM), has made substantial progress, most classic algorithms in this field are designed based on the assumption that the observed scene is static. In complex real-world environments, the presence of dynamic objects such as pedestrians and vehicles can seriously affect the robustness and accuracy of such systems. Event cameras, which use recently introduced motion-sensitive biomimetic sensors, efficiently capture scene changes (referred to as “events”) with high temporal resolution, offering new opportunities to enhance VI-SLAM performance in dynamic environments. Integrating this kind of innovative sensor, we propose the first event-enhanced Visual–Inertial SLAM framework specifically designed for dynamic environments, termed E[sup.2]-VINS. Specifically, the system uses visual–inertial alignment strategy to estimate IMU biases and correct IMU measurements. The calibrated IMU measurements are used to assist in motion compensation, achieving spatiotemporal alignment of events. The event-based dynamicity metrics, which measure the dynamicity of each pixel, are then generated on these aligned events. Based on these metrics, the visual residual terms of different pixels are adaptively assigned weights, namely, dynamicity weights. Subsequently, E[sup.2]-VINS jointly and alternately optimizes the system state (camera poses and map points) and dynamicity weights, effectively filtering out dynamic features through a soft-threshold mechanism. Our scheme enhances the robustness of classic VI-SLAM against dynamic features, which significantly enhances VI-SLAM performance in dynamic environments, resulting in an average improvement of 1.884% in the mean position error compared to state-of-the-art methods. The superior performance of E[sup.2]-VINS is validated through both qualitative and quantitative experimental results. To ensure that our results are fully reproducible, all the relevant data and codes have been released.
Journal Article
MDU‐sampling: Multi‐domain uniform sampling method for large‐scale outdoor LiDAR point cloud registration
Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi‐domain uniform sampling method (MDU‐sampling) for large‐scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains, reducing the computational resources for registration. The proposed method preserves more effective information compared to other algorithms. Points are only considered on the same ring in LiDAR data as neighbouring points, eliminating the need for additional neighbouring point search. This makes it efficient and suitable for large‐scale outdoor LiDAR point cloud registration.
Journal Article