25 accepted papers
Full-body motion estimation from sparse VR observations is an inherently under-constrained problem, with only three 6-DoF trackers (HMD and controllers) available to infer a full skeletal pose. To address this ambiguity, we introduce a probabilistic framework that models joint orientations as distri
Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vision-based methods due to occlusions and constrained capture volumes. Inertial measurement units (IMUs) offer a compelling alternative without line-
Millimeter-wave radar offers a privacy-preserving and environment-robust alternative to vision-based sensing, enabling human motion analysis in challenging conditions such as low light, occlusions, rain, or smoke. However, its sparse point clouds pose significant challenges for semantic understandin
Recent advances in large vision-language models (VLMs) and large language models (LLMs) have enabled zero-shot approaches to Visual Language Navigation (VLN), where an agent follows natural language instructions using only ego perception and reasoning. However, existing zero‑shot methods typically c…
Accurate 3D reconstruction is crucial for AR and VR applications. Compared with traditional pinhole camera-based methods, 360° image-based reconstruction can achieve higher precision with fewer input images, making it especially effective in low-texture environments. However, the severe distortion r
Recently, multi-node inertial measurement unit (IMU)-based odometry for legged robots has gained attention due to its cost-effectiveness, power efficiency, and high accuracy. However, the spatial and temporal misalignment between foot-end motion derived from forward kinematics and foot IMU measureme
Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (…
Traversability estimation is the foundation of path planning for a general navigation system. However, complex and dynamic environments pose challenges for the latest methods using self-supervised learning (SSL) technique. Firstly, existing SSL-based methods generate sparse annotations lacking detai
Global Navigation Satellite System (GNSS) suffers severe accuracy degradation in urban environments due to Non-Line-of-Sight (NLOS) and multipath effects. Several methods have been proposed to detect and mitigate NLOS/multipath, but those rely on additional equipment, high costs, and limited multipa
The task of occupancy forecasting (OCF) involves utilizing past and present perception data to predict future occupancy states of autonomous vehicle surrounding environments, which is critical for downstream tasks such as obstacle avoidance and path planning. Existing 3D OCF approaches struggle to p…
With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, whil
Event-based visual odometry has recently gained attention for its high accuracy and real-time performance in fast-motion systems. Unlike traditional synchronous estimators that rely on constant-frequency (zero-order) triggers, event-based visual odometry can actively accumulate information to genera
Millimeter-wave (mmWave) radar offers robust sensing capabilities in diverse environments, making it a highly promising solution for human body reconstruction due to its privacy-friendly and non-intrusive nature. However, the significant sparsity of mm Wave point clouds limits the estimation accurac
This study presents a novel approach to enhance the accuracy and adaptability of pedestrian positioning by fusing data from multiple Inertial Measurement Units (IMUs) attached to the human body. Leveraging the temporal and spatial richness of IMU data, our proposed multi-node sensors fusion strategy…
This paper introduces a novel human pose estimation approach using sparse inertial sensors addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and mode…
Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. Wh…
In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant…
Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions. Despite pioneering efforts in this field, tw…
Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance field…
Invariant Extended Kalman Filter (IEKF) has been successfully applied in Visual-inertial Odometry (VIO) as an advanced achievement of Kalman filter, showing great potential in sensor fusion. In this paper, we propose partial IEKF (PIEKF), which only incorporates rotation-velocity state into the Lie…
Precise Point Positioning (PPP), a cutting edge GNSS technology, can achieve high-precision positioning without base station assistance. Visual-Inertial Odometry (VIO) realizes a more robust local pose estimation than Visual-SLAM. Based on PPP and VIO, we propose a tightly-coupled PPP/INS/Visual SLA
We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline. The key idea of our novel text-based visual SLAM is to treat each detected text as a planar feature which is rich of textures and semantic meanings. The text feature is compactly represented by three param…
Quadrotor state estimation generally relies on the vehicle aerodynamics modeling to achieve improved performance. In this paper the effects of the rotors angular speeds on the quadrotor drag, and therefore on the lateral accelerations, are investigated. While these effects are usually disregarded, w…
A robust state estimator is presented by fusing the aerodynamic model of multi-rotor UAVs with measurements from optical flow and other low-cost sensors such as IMU, magnetometer, and ultrasonic sensor. Due to the particular aerodynamics of multi-rotor UAVs, the body velocity in the rotor plane is a…