Neural Radiance Fields (NeRF)-based Visual Simultaneous Localization and Mapping (SLAM) achieve superior scene geometric modeling and robust camera tracking by leveraging neural representations. Existing methods typically relied on multi-resolution hash encoding with truncated signed distance fields (TSDF) to achieve high frame rates. However, unavoidable hash collisions can lead to artifacts, and multi-view color inconsistencies in indoor scenes can result in shape-radiance ambiguity, adversely affecting geometric quality and tracking accuracy. To address these issues, we propose a novel Multi-scale Hybrid Encoding-based Decoupled SLAM (MHED-SLAM). First, to mitigate the adverse effects of hash collisions and reduce the number of learnable parameters, we innovatively fuse a coarse-scale hash tri-plane with a fine-scale hash grid within a single latent volume. Second, to enable precise geometric reconstruction and camera tracking, we decouple the reconstruction and rendering processes, independently learning a TSDF field for reconstruction and a density field for rendering. Third, we devise a Symmetric Kullback-Leibler (SKL) strategy based on ray termination distributions to align the probability distributions derived from the TSDF and density fields for their synchronous convergence. Extensive experimental evaluations demonstrate that our approach surpasses the state-of-the-art (SOTA) methods by utilizing a faster frame rate of 20 Hz and fewer parameters, while achieving higher tracking and reconstruction accuracy.

Augmented reality (AR), a prominent application within the Internet of Things (IoT) domain, demands high-performance pose estimation. Presently, the visual-inertial navigation system (VINS) is acknowledged as an essential method for providing 6-DoF poses. However, VINS builds the local frame at random during the system initialization stage, making it difficult to establish a connection with the global frame. In addition, VINS is prone to drifting. In this paper, we propose an innovative method that tightly couples markerless motion capture (Mocap) with vision and an IMU to achieve global and drift-free pose estimation for AR glasses. To address the issue of pose initialization and establish a connection between the IMU and Mocap, we introduce a coarse-to-fine initialization strategy, enabling data fusion for Mocap, vision, and the IMU under a unified global frame. Furthermore, we formulate the Mocap factor alongside the visual and inertial factors and integrate them into a factor graph framework to constrain the system states. With a spatiotemporal calibration method, the IMU-Mocap extrinsic parameter and time offset are calibrated online to improve the pose estimation accuracy. Experimental evaluations in real-world experiments demonstrate the capability of our method to accurately estimate drift-free poses in the global frame. Compared to the state-of-the-art VINS-Fusion, ORB-SLAM3, and GVIS, we achieve improvements of 81%, 42%, and 33% in translation accuracy and improvements of 58%, 33%, and 72% in rotation accuracy, respectively. Moreover, we also evaluate our system for the EuRoC dataset, further indicating the effectiveness of the proposed work.

Abstract In order to align the virtual and real content precisely through augmented reality devices, especially in optical see-through head-mounted displays (OST-HMD), it is necessary to calibrate the device before using it. However, most existing methods estimated the parameters via 3D-2D correspondences based on the 2D alignment, which is cumbersome, time-consuming, theoretically complex, and results in insufficient robustness. To alleviate this issue, in this paper, we propose an efficient and simple calibration method based on the principle of directly calculating the projection transformation between virtual space and the real world via 3D-3D alignment. The proposed method merely needs to record the motion trajectory of the cube-marker in the real and virtual world, and then calculate the transformation matrix between the virtual space and the real world by aligning the two trajectories in the observed view. There are two advantages associated with the proposed method. First, the operation is simple. Theoretically, the user only needs to perform four alignment operations for calibration without changing the rotation variation. Second, the trajectory can be easily distributed throughout the entire observation view, resulting in more robust calibration results. To validate the effectiveness of the proposed method, we conducted extensive experiments on our self-built optical see-through head-mounted display (OST-HMD) device. The experimental results show that the proposed method can achieve better calibration results than other calibration methods.