In the metaverse, digital avatar plays an important role in representing human beings for various interaction with virtual objects and environments, which puts a high demand on effective pose estimation. Though camera-based solutions yield remarkable performance, they encounter privacy issues and degraded performance caused by varying illumination, especially in the smart home. In this article, we propose a WiFi-based Internet of Things-enabled human pose estimation scheme for metaverse avatar simulation, namely, MetaFi++. Specifically, WPFormer is designed with a shared convolutional module and a Transformer block to map the channel state information of WiFi signals to human pose landmarks, effectively exploring spatial information of human pose through self-attention. It is enforced to learn the annotations from the accurate computer vision model, thus achieving cross-modal supervision. Due to the ubiquitous existence of WiFi and robustness to various illumination conditions, WiFi-based human poses are suitable to instruct the movement of digital avatars in the metaverse, promoting avatar applications in smart homes. The experiments are conducted in the real world, and the results show that the MetaFi++ achieves very high performance with a PCK@50 of 97.30%. Our codes are available in https://github.com/pridy999/metafi_pose_estimation .

This work proposes an incremental Generalized Iterative Closest Point (GICP) based tightly-coupled LiDAR-inertial odometry (LIO), iG-LIO, which integrates the GICP constraints and inertial constraints into a unified estimation framework. iG-LIO uses a voxel-based surface covariance estimator to estimate the surface covariances of scans, and utilizes an incremental voxel map to represent the probabilistic models of surrounding environments. These methods successfully reduce the time consumption of the covariance estimation, nearest neighbor search, and map management. Extensive datasets collected from mechanical LiDARs and solid-state LiDARs are employed to evaluate the efficiency and accuracy of the proposed LIO. Even though iG-LIO keeps identical parameters across all datasets, the results show that it is more efficient than Faster-LIO while maintaining comparable accuracy with state-of-the-art LIO systems. The source code for iG-LIO has been open-sourced on GitHub: https://github.com/zijiechenrobotics/ig_lio .

In this paper, we present an efficient visual SLAM system designed to tackle both short-term and long-term illumination challenges. Our system adopts a hybrid approach that combines deep learning techniques for feature detection and matching with traditional backend optimization methods. Specifically, we propose a unified convolutional neural network (CNN) that simultaneously extracts keypoints and structural lines. These features are then associated, matched, triangulated, and optimized in a coupled manner. Additionally, we introduce a lightweight relocalization pipeline that reuses the built map, where keypoints, lines, and a structure graph are used to match the query frame with the map. To enhance the applicability of the proposed system to real-world robots, we deploy and accelerate the feature detection and matching networks using C++ and NVIDIA TensorRT. Extensive experiments conducted on various datasets demonstrate that our system outperforms other state-of-the-art visual SLAM systems in illumination-challenging environments. Efficiency evaluations show that our system can run at a rate of 73Hz on a PC and 40Hz on an embedded platform. Our implementation is open-sourced: https://github.com/sair-lab/AirSLAM.

Estimating the robot's heading is a crucial requirement in odometry systems which are attempting to estimate the movement trajectory of a robot. Small errors in the orientation estimation result in a significant difference between the estimated and real trajectory, and failure of the odometry system. The odometry problem becomes much more complicated for micro flying robots since they cannot carry massive sensors. In this manner, they should benefit from the small size and low-cost sensors, such as IMU, to solve the odometry problem, and industries always look for such solutions. However, IMU suffers from bias and measurement noise, which makes the problem of position and orientation estimation challenging to be solved by a single IMU. While there are numerous studies on the fusion of IMU with other sensors, this study illustrates the power of the first deep learning framework for estimating the full 3D orientation of the flying robots (as yaw, pitch, and roll in quaternion coordinates) accurately with the presence of a single IMU. A particular IMU should be utilized during the training and testing of the proposed system. Besides, a method based on the Genetic Algorithm is introduced to measure the IMU bias in each execution. The results show that the proposed method improved the flying robots' ability to estimate their orientation displacement by approximately 80% with the presence of a single particular IMU. The proposed approach also outperforms existing solutions that utilize a monocular camera and IMU simultaneously by approximately 30%.

As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection, and other human identification applications. Presently, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this article, we propose a novel multimodal gait recognition method, namely, GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, channel state information (CSI) that reflects the multipath propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a lightweight residual convolution network (LRCN) as the backbone network and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.

Despite recent progress in trajectory planning for multiple robots and a single tethered robot, trajectory planning for multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this article, a complete approach is presented to address this problem. First, a multirobot tether-aware representation of homotopy is proposed to efficiently evaluate the feasibility and safety of a potential path in terms of 1) the cable length required to reach a target following the path, and 2) the risk of entanglements with the cables of other robots. Then the proposed representation is applied in a decentralized and online planning framework, which includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free, and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against the existing single and multiple tethered robot planning approaches. Simulations with up to eight UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using three tethered UAVs verify the practicality of the presented approach. The software implementation is publicly available online. ¹