573 Results Found

One of the most fundamental tasks for robots inspecting water distribution pipes is localization, which allows for autonomous navigation, for faults to be communicated, and for interventions to be instigated. Pose-graph optimization using spatially v...

We introduce the pose-graph topological integrity, an approach designed to assess the correctness of pose graphs in simultaneous localization and mapping. Traditional methods assessed map quality according to the optimality criteria based on pose gra...

In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning se...

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visua...

Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative m...

Monocular 3D human pose estimation is used to calculate a 3D human pose from monocular images or videos. It still faces some challenges due to the lack of depth information. Traditional methods have tried to disambiguate it by building a pose diction...

With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the p...

Existing whole-body human pose estimation methods mostly segment the parts of the body’s hands and feet for specific processing, which not only splits the overall semantics of the body, but also increases the amount of calculation and the compl...

To address the problem of low accuracy in joint point estimation in hand pose estimation methods due to the self-similarity of fingers and easy self-obscuration of hand joints, a hand pose estimation method based on coordinate correction and graph co...

Human pose estimation is a core computer vision task with broad applications, yet its performance degrades significantly in crowded scenes and under heavy occlusion due to missing or unreliable visual evidence. To address this limitation, this work r...

In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which t...

The analysis of hand–object poses from RGB images is important for understanding and imitating human behavior and acts as a key factor in various applications. In this paper, we propose a novel coarse-to-fine two-stage framework for hand–...

This work addresses the loop closure detection issue by matching the local pose graphs for semantic visual SLAM. We propose a deep feature matching-based keyframe retrieval approach. The proposed method treats the local navigational maps as images. T...

With the rapid advancement of deep learning, 3D human pose estimation has largely freed itself from reliance on manually annotated methods. The effective utilization of joint features has become significant. Utilizing 2D human joint information to pr...

Multi-camera Visual Simultaneous Localization and Mapping (V-SLAM) increases spatial coverage through multi-view image streams, improving localization accuracy and reducing data acquisition time. Despite its speed and generally robustness, V-SLAM oft...

To address the problem of poor detection and under-utilization of the spatial relationship between nodes in human pose estimation, a method based on an improved spatial temporal graph convolutional network (ST-GCN) model is proposed. Firstly, upsampl...

Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction,...

Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dea...

Human Pose Estimation (HPE) aims to accurately locate the positions of human key points in images or videos. However, the performance of HPE is often significantly reduced in practical application scenarios due to environmental interference. To addre...

The development of 2D-to-3D approaches for 3D monocular single-frame human pose estimation faces challenges related to noisy input and failure to capture long-range joint correlations, leading to unreasonable predictions. To this end, we propose a st...

In the field of computer vision, hand pose estimation (HPE) has attracted significant attention from researchers, especially in the fields of human–computer interaction (HCI) and virtual reality (VR). Despite advancements in 2D HPE, challenges...

Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process...

Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. When a robotic arm guides a laparoscope for image acquisition, hand–eye calibration is required to k...

Multi-person pose forecasting involves predicting the future body poses of multiple individuals over time, involving complex movement dynamics and interaction dependencies. Its relevance spans various fields, including computer vision, robotics, huma...

Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expens...

In response to the inaccurate positioning of traditional camera relocation methods in scenes with large-scale or severe viewpoint changes, this study proposes a camera relocation method based on a cascaded graph neural network to achieve accurate sce...

This paper presents a real-time, robust and low-drift depth-only SLAM (simultaneous localization and mapping) method for depth cameras by utilizing both dense range flow and sparse geometry features from sequential depth images. The proposed method i...

The current visual simultaneous localization and mapping (SLAM) systems require the use of matched feature point pairs to estimate camera pose and construct environmental maps. Therefore, they suffer from poor performance of the visual feature matche...

Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience. The aim of our work...

Accurate 6D object pose estimation is critical for autonomous docking. To address the inefficiencies and inaccuracies associated with maximal cliques-based pose estimation methods, we propose a fast 6D pose estimation algorithm that integrates featur...

In intensive cage rearing systems, accurate dead hen detection remains difficult due to complex environments, severe occlusion, and the high visual similarity between dead hens and live hens in a prone posture. To address these issues, this study pro...

Human pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human–computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the imag...

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating env...

In this paper, we present a novel approach for reconstructing 3D geometry from a stream of images captured by a consumer-grade mobile RGB-D sensor. In contrast to previous real-time online approaches that process each incoming image in acquisition or...

In category-level object pose estimation tasks, determining how to mitigate intra-class shape variations and improve pose estimation accuracy for complex objects remains a challenging problem to solve. To address this issue, this paper proposes a new...

The novel contribution of this paper is to propose an incremental pose map optimization for monocular vision simultaneous localization and mapping (SLAM) based on similarity transformation, which can effectively solve the scale drift problem of SLAM...

Human fall detection is a significant healthcare concern, particularly among the elderly, due to its links to muscle weakness, cardiovascular issues, and locomotive syndrome. Accurate fall detection is crucial for timely intervention and injury preve...

Despite the great progress in 3D pose estimation from videos, there is still a lack of effective means to extract spatio-temporal features of different granularity from complex dynamic skeleton sequences. To tackle this problem, we propose a novel, s...

Indoor localization is a basic element in location-based services (LBSs), including seamless indoor and outdoor navigation, location-based precision marketing, spatial recognition in robotics, augmented reality, and mixed reality. The popularity of L...

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose...

Traditional cardiopulmonary resuscitation (CPR) training faces limitations such as instructor dependency, low efficiency, and subjective assessment. To address these issues, this study proposes a novel computer vision-based method for the automation...

Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize back...

The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive envir...

The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require coo...

The paper presents a simple, yet robust computer vision system for robot arm tracking with the use of RGB-D cameras. Tracking means to measure in real time the robot state given by three angles and with known restrictions about the robot geometry. Th...

Indoor service robots need to build an object-centric semantic map to understand and execute human instructions. Conventional visual simultaneous localization and mapping (SLAM) systems build a map using geometric features such as points, lines, and...

Collaboration between autonomous vehicles and drones can enhance the efficiency and connectivity of three-dimensional transportation systems. When satellite signals are unavailable, vehicles can achieve accurate localization by matching rich ground e...

Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magn...

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling...

Accurate, robust and drift-free global pose estimation is a fundamental problem for autonomous vehicles. In this work, we propose a global drift-free map-based localization method for estimating the global poses of autonomous vehicles that integrates...