Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a f...
In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics e...
For upper extremity rehabilitation, quantitative measurements of a person’s capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a per...
We propose a novel pose estimation method that can predict the full-body pose from six inertial sensors worn by the user. This method solves problems encountered in vision, such as occlusion or expensive deployment. We address several complex challen...
Visual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, t...
Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the d...
A visual–inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transf...
The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural ne...
The motion capture method using sparse inertial sensors is an approach for solving the occlusion and economic problems in vision-based methods, which is suitable for virtual reality applications and works in complex environments. However, VR applicat...
The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and the...
Wearable inertial measurement units are widely used for human motion analysis due to their portability; however, most IMU-based motion capture systems rely on dense sensor configurations that increase cost, complexity, and usability challenges in rea...
Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This paper presents a wireless communication-based indoor localization method that...
Underwater positioning presents a challenging issue, because of the rapid attenuation of electronic magnetic waves, the disturbances and uncertainties in the environment. Conventional methods usually employed acoustic devices to localize Unmanned Und...
Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high...
Marine robots operating in low illumination and turbid waters require reliable measurement and control for surveying, inspection, and monitoring. This paper present a sensor-centric visual–inertial simultaneous localization and mapping (SLAM) p...
Autonomous navigation of underwater vehicles in infrastructure-limited environments presents persistent challenges due to the constraints of traditional acoustic positioning systems. Sparse long baseline (sparse LBL) navigation, which relies on a min...
This paper proposes a practical calibration solution for estimating the boresight and lever-arm parameters of the sensors mounted on a Mobile Mapping System (MMS). On our MMS devised for conducting the calibration experiment, three network video came...
An off-line smoother algorithm is proposed to estimate foot motion using an inertial sensor unit (three-axis gyroscopes and accelerometers) attached to a shoe. The smoother gives more accurate foot motion estimation than filter-based algorithms by us...
The center of pressure (CoP) is a key biomechanical indicator for assessing balance and fall risk; however, force plates, the gold standard for CoP measurement, are costly and impractical for widespread use. Low-cost alternatives such as inertial uni...
Indoor localization remains a challenging task due to the unavailability of reliable global navigation satellite system (GNSS) signals in most indoor environments. One way to overcome this challenge is through visual–inertial odometry (VIO), wh...
Reliable wearable motion tracking depends not only on nominal sensor precision but on whether calibration remains valid after re-donning, anatomical misalignment, magnetic disturbance, temperature change, and prolonged operation. This structured crit...
Indoor mobile robots equipped with low-cost and sparse sensors often suffer from limited vertical perception and dynamic residual artifacts in the final map. This paper presents a lightweight 2.5D simultaneous localization and mapping (SLAM) framewor...
A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft bi...
There is a high prevalence of shoulder problems in manual wheelchair users (MWUs) with a spinal cord injury. How shoulder load relates to shoulder problems remains unclear. This study aimed to develop a machine-learning-based methodology to estimate...
In this paper, we address a system that can accurately locate and monitor work tools in a complex assembly process, such as automotive production. Our positioning monitoring system is positioned by a combined sensor of the UWB module and the MEMS IMU...
Accurate and reliable vehicle localization is essential for autonomous driving in complex outdoor environments. Traditional feature-based visual–inertial odometry (VIO) suffers from sparse features and sensitivity to illumination, limiting robu...
Animal locomotion exhibits highly structured temporal dynamics despite significant inter-species biomechanical and anatomical discrepancies. While wearable inertial measurement unit (IMU) sensors and deep learning have advanced animal activity recogn...
Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory...
Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation...
Accurate full-body motion reconstruction from sparse sensors is crucial for VR/AR applications but remains challenging due to the under-constrained nature of limited observations and the computational constraints of mobile platforms. This paper prese...
Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite...
Visual–inertial odometry (VIO) often struggles with illumination variations, sparse visual features, and inertial drift in complex indoor settings, leading to scale uncertainties and accumulated errors. To address these issues, this paper propo...
Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To addres...
This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS d...
Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) us...
Achieving long-term, continuous, and accurate localization for Unmanned Aerial Vehicles (UAVs) in outdoor GNSS-denied environments where pre-existing reference maps are available is challenging. To this end, this paper proposes a Cross-view Particle...
The rapid measurement of three-dimensional bridge geometric shapes is crucial for assessing construction quality and in-service structural conditions. Existing geometric shape measurement methods predominantly rely on traditional surveying instrument...
This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to...
Low-cost microelectromechanical-system inertial measurement units (MEMS IMUs) enable self-contained vehicle navigation, but their errors accumulate rapidly when global navigation satellite system (GNSS) updates are unavailable. Nonholonomic constrain...
Commercialization of autonomous vehicle technology is a major goal of the automotive industry, thus research in this space is rapidly expanding across the world. However, despite this high level of research activity, literature detailing a straightfo...
This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental co...
This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The propose...
Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such applications, LiDAR and frame came...
In human activity recognition (HAR), activities are automatically recognized and classified from a continuous stream of input sensor data. Although the scientific community has developed multiple approaches for various sports in recent years, margina...
Accurate gait event detection using inertial measurement units (IMUs) is essential for temporal gait analysis, but frame-level detection is challenged by sparse initial contact (IC) and foot-off (FO) events. This study evaluated recurrent neural netw...
Navigation is a precondition for ocean space vehicles to work safely in polar regions. The traditional polar algorithms employ the grid strapdown inertial navigation system (SINS) as the backbone and Doppler velocity log (DVL) output velocity as meas...
Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation app...
Response latency and sensor noise are universal challenges in closed-loop control systems. In the context of semi-active suspensions, these issues also exist and manifest as critical bottlenecks. Due to the highly transient nature of road shocks, the...
The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allo...
Vision-based sensors such as LiDAR (Light Detection and Ranging) are adopted in the SLAM (Simultaneous Localization and Mapping) system. In the 16-beam LiDAR aided SLAM system, due to the difficulty of object detection by sparse laser data, neither t...
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