Our previous work1 introduced a visual-inertial odometry (VIO) method that leverages long-tracked drifted features, which is able to perform precise localization in complex environments. However, a key limitation is its reliance on computationally intensive large sliding window filter (SWF) to maintain performance. By analyzing the observability of the original system, we shown that the consistency of the SWF is disrupted due to the discrepancy between the linearization points of the prior state in the historical SWF and the current one. In this case, the covariance matrix of the estimation errors is larger than that predicted by the SWF, leading to inaccurate state updates and loss of accuracy. The original system achieves delayed marginalization by increasing the capacity of the SWF, thereby reducing the discrepancy of the linearization points and, in turn, minimizing the disruption to the consistency of the SWF. In this paper, we adopt a more efficient strategy by aligning the null space matrix of the prior with that of the current SWF, thereby avoiding the destruction of the consistency. We evaluate our method on the EuRoC, TUM-VI, and 4Seasons datasets, comparing it with state-of-the-art methods. Experimental results show that our approach achieves high localization accuracy even with smaller SWF, making it more suitable for non-terrestrial networks (NTN)-based deployments with resource constraints. The code will be published on GitHub.2.

This paper presents a visual-inertial odometry (VIO) method using long-tracked features. Long-tracked features can constrain more visual frames, reducing localization drift. However, they may also lead to accumulated matching errors and drift in feature tracking. Current VIO methods adjust observation weights based on re-projection errors, yet this approach has flaws. Re-projection errors depend on estimated camera poses and map points, so increased errors might come from estimation inaccuracies, not actual feature tracking errors. This can mislead the optimization process and make long-tracked features ineffective for suppressing localization drift. Furthermore, long-tracked features constrain a larger number of frames, which poses a significant challenge to real-time performance of the system. To tackle these issues, we propose an active decoupling mechanism for accumulated errors in long-tracked feature utilization. We introduce a visual reference frame reset strategy to eliminate accumulated tracking errors and a depth prediction strategy to leverage the long-term constraint. To ensure real-time performance, we implement three strategies for efficient system state estimation: a parallel elimination strategy based on predefined elimination order, an inverse-depth elimination simplification strategy, and an elimination skipping strategy. Experiments on various datasets show that our method offers higher positioning accuracy with relatively short consumption time, making it more suitable for edge-enabled low-altitude IoT navigation, where high-accuracy positioning and real-time operation on edge device are required.

Integrated sensing and communication (ISAC) is regarded as a promising solution for the development of the emerging low-altitude economy (LAE). Given the necessity of accurate and realistic wireless channel models for ISAC evaluation and optimization, this paper proposes an LAE-oriented ISAC channel modeling framework. By incorporating the target unmanned aerial vehicle (UAV) scattering response, we decouple the ISAC channel into target and background channels. For the target channel, the model is formulated as a cascade of the transmitting base station (BS)-target link, the target-sensing BS link, and the scattering response from the target UAV. For the background channel, a novel parameter is introduced to separate the influence of the target UAV. Furthermore, key statistical properties, including the space-time-frequency correlation function, coherence distance, Doppler power spectral density, root mean square delay spread, and stationarity interval, are derived and analyzed. The accuracy of the proposed channel model is verified by the close agreement between simulated statistics properties and measured data, and the effectiveness of the cascaded method is validated by the close agreement between the concatenation output and simulation results.

In this paper, we present a novel method by using a sliding window filter (SWF) for real-time kinematic (RTK)-visual-inertial navigation. Unlike other recent works that retain only the states of visual keyframes to reduce computational complexity, we additionally retain the GNSS states (i.e., position, orientation, and velocity of the body and inertial biases at the time of capturing GNSS measurements) in the SWF to construct more appropriate constraints between measurements and states. In order to make the method run as a real-time system, especially when the SWF contains numerous GNSS states, we propose a parallel elimination strategy in a predefined elimination ordering, which can solve the Gauss-Newton problem and simultaneously obtain the covariance for ambiguity resolution (AR). We reveal when and how the system improves the AR performance. Moreover, we analyze the observability of the system under different conditions. We also conduct experiments in the real world and compare the results with the state-of-the-arts. Experimental results show that the proposed method is able to achieve a higher and stabler fixed rate in GNSS challenging environments, and has better positioning performance with or without measurements of a base station. We have decided to publish the code of our work for the community1.

Abstract In order to eliminate the influence of acoustic velocity variable on the estimation accuracy when the signal propagates under water, this paper proposes a velocity‐independent two‐dimensional (2‐D) direction‐of‐arrival (DOA) estimation algorithm with three parallel uniform linear arrays. Based on the double parallel linear arrays (DPLA), this algorithm adds another parallel linear array that is not in the same plane as DPLA, which is called three parallel linear arrays. By employing the matrix signal processing among those three arrays, the acoustic velocity variable can be removed from the expressions of elevation and azimuth. And the azimuth and elevation angles of the target are obtained by linear partitive operation, which does not need spectrum peak search or additional angle matching procedure. The simulation results demonstrate that the proposed algorithm performs better than the traditional 2‐D DOA algorithms in the underwater environment of unknown acoustic velocity. Compared with the velocity‐independent 2‐D DOA algorithm, it has lower computational complexity.

Abstract Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over‐smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window‐aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non‐box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity‐similar strategy, the local non‐box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non‐box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.