The autonomous landing of unmanned aerial vehicles (UAVs) relies on a precise relative 6D pose between platforms. Existing model-based monocular pose estimation methods need an accurate 3D model of the target. They cannot handle the absence of an acc...
Seniors who live alone at home are at risk of falling and injuring themselves and, thus, may need a mobile robot that monitors and recognizes their poses automatically. Even though deep learning methods are actively evolving in this area, they have l...
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...
Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In...
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...
Satellite pose estimation (PE) is crucial for space missions and orbital maneuvering. High-accuracy satellite PE could reduce risks, enhance safety, and help achieve the objectives of close proximity and docking operations for autonomous systems by r...
Although industrial robots are widely used in production automation, their applications in machining have been limited because of the structural vibrations induced by periodic cutting forces. Since the dynamic characteristics of an industrial robot d...
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...
Three-dimensional (3D) human pose sequences often exhibit coordinate noise, inconsistent bone lengths, and temporal jitter, which cannot be adequately assessed using positional error alone. We propose an offline kinematic–temporal refinement fr...
Far-field vision-based pose measurement is a crucial technology for applications such as high-resolution Earth observation and space security early warning. However, owing to the perspective imaging model of long-range optical systems, conventional v...
NeRF has garnered extensive attention from researchers due to its impressive performance in three-dimensional scene reconstruction and realistic rendering. It is perceived as a potential pivotal technology for scene reconstruction in fields such as v...
This paper presents an efficient grasp pose estimation algorithm for robotic arm systems with a two-finger parallel gripper and a consumer-grade depth camera. Unlike traditional deep learning methods, which suffer from high data dependency and ineffi...
Accurate relative pose estimation between unmanned aerial vehicles (UAVs) is a key requirement for cooperative navigation, formation control, and swarm operation in GNSS-denied environments. In multi-UAV systems, monocular vision is attractive due to...
This study introduces a real-time, non-intrusive monitoring system designed to support elderly care through vision-based pose estimation and hand gesture recognition. The proposed framework integrates convolutional neural networks (CNNs), temporal mo...
Accurate extrinsic calibration among multiple heterogeneous Light Detection and Ranging (LiDAR) sensors is essential for autonomous vehicle perception systems, yet remains challenging in distributed topologies where overlap exists only between adjace...
In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstru...
In order to enhance the stability of vehicle pose estimation within driving videos, a novel methodology for optimizing vehicle structural parameters is introduced. This approach hinges on evaluating the reliability of edge point sequences. Firstly, a...
Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent...
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...
In this study, a manipulator calibration algorithm is suggested to decrease the positional errors of an industrial robotic manipulator using a genetic algorithm to select optimal measurement poses. First, a genetic algorithm based on the observabilit...
In this study, a kinematic calibration method is proposed. The method selects the optimal measurement poses based on the observability index O1, and is used for six-hardpoint positioning mechanisms, which can identify and compensate for kinematic par...
The turning function is a tool in image processing that measures the difference between two polygonal shapes. We propose a localization algorithm for the optimal pose estimation of autonomous mobile robots using the scan-matching method based on the...
Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a commo...
In this paper, we examine a method for improving pose estimation by correctly positioning the sensors relative to the scanned object. Three objects made of different materials and using different manufacturing technologies were selected for the exper...
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...
The adaptability of a hexapod robot to complex terrain is highly dependent on its own posture, which directly affects its stability and flexibility. In order to adapt to a change in terrain, it is necessary to adjust posture in real time when walking...
LiDAR SLAM is widely used in robotic navigation and autonomous driving, but many existing methods still handle frontend point cloud processing and backend pose optimization as two loosely connected stages with fixed settings. This can lead to unneces...
Few-shot, multi-pose face recognition has always been an interesting yet difficult subject in the field of pattern recognition. Researchers have come up with a variety of workarounds; however, these methods make it either difficult to extract effecti...
Digital twins are essential for the real-time health management and monitoring of aeroengines, and the in situ three-dimensional (3D) reconstruction technology of key components of aeroengines is an important support for the construction of a digital...
Accurate and real-time outdoor localization and pose estimation are critical for various applications, including navigation, robotics, and augmented reality. Apple’s ARKit, a leading AR platform, employs visual–inertial odometry (VIO) and...
Six degrees-of-freedom (6D) object pose estimation plays an important role in pattern recognition of fields such as robotics and augmented reality. However, there are issues with low accuracy and real-time performance of 6D object pose estimation in...
Six degrees of freedom pose estimation technology constitutes the cornerstone for precise robotic control and similar tasks. Addressing the limitations of current 6-DoF pose estimation methods in handling object occlusions and unknown objects, we hav...
The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and buil...
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...
This paper presents a novel architecture of the vision/position hybrid control for a Hexa parallel robot. The 3D vision system is combined with the Proportional-Integral-Derivative (PID) position controller to form a two-level closed-loop controller...
Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp g...
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...
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...
Pose estimation is one of the most complicated and compromising problems for underground mining machine tracking, and it is particularly important for hydraulic support autonomous following mining machine (AFM) policy-making system. In this paper, a...
To solve the problem of the insufficient accuracy and stability of the two-stage pose estimation algorithm using heatmap in the problem of occluded object pose estimation, a new robust 6-DoF pose estimation algorithm under hybrid constraints is propo...
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...
This paper investigates the well-posedness of the inverse problem for the time-fractional Burgers equation, which aims to reconstruct initial conditions from terminal observations. Such equations are crucial for the modeling of hydrodynamic phenomena...
Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range mea...
The AC rotary head, serving as a dual-axis direct-drive rotary actuation unit in five-axis CNC machine tools, integrates torque motors for A- and C-axis actuation, and its structural static and dynamic characteristics directly govern the actuation ac...
One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the co...
Traditional SLAM methods face significant challenges in complex environments, including high computational complexity, ambiguous data association, and limited real-time performance. Existing approaches often rely on explicit data association or compu...
Accurate golf swing correction requires modeling not only static pose deviations but also temporal rhythm and biomechanical stability throughout the swing sequence. Most existing pose-based approaches rely on frame-wise similarity and therefore fail...
In two-dimensional laser SLAM exploration and mapping in complex indoor environments, scan matching is sensitive to the initial pose, error accumulation, and redundant map updates. To address these issues, an MDF-ALC-SLAM method is proposed. Firstly,...
Passenger-gathering behavior often triggers safety incidents such as stampedes due to overcrowding, posing significant challenges to public order maintenance and passenger safety. Traditional early warning algorithms for passenger-gathering behavior...
This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framewo...
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