Direct-sequence spread spectrum (DSSS) communication, as a key technology in code-division multiple access networks, demonstrates excellent performance in multiple-access capability and interference suppression. However, its effectiveness significantly degrades in mobile underwater acoustic (UWA) networks, where the platform mobility induces severe Doppler effect and exacerbates the instability of multipath propagation. These impairments are further exacerbated by the inherently low data rates of UWA DSSS systems, where the prolonged signal duration leads to time-varying Doppler effect and fast-fading multipath structure within a single frame, collectively degrading the UWA DSSS receiver performance. To address these limitations, this paper proposes an improved DSSS receiver to achieve time-varying UWA multipath tracking, which employs Kalman filtering for channel parameter estimation. A multipath management technique is implemented to determine the appearance and disappearance of paths, followed by a coherent combination of all valid paths. Comprehensive simulations demonstrate the superior performance of the proposed receiver under various time-varying channel scenarios. The effectiveness of the proposed receiver is further validated by experimental data collected from Danjiangkou Lake and Zhang River experiments.

Accurate and robust place recognition is essential for robot navigation, yet achieving full pose invariance and high performance across diverse scenes remains challenging. In this work, we propose a novel global and local combined descriptor named Binary Triangle Combined (BTC) descriptor. We first extract the keypoints of a point cloud by projecting the points to planes extracted therein. Any three keypoints form a unique triangle, with the lengths of its sides constituting a triangle descriptor that captures the global appearance of the point cloud. Thanks to the distinct shape of a triangle given three side lengths, the similarity between two triangles and their vertices (i.e., keypoints) correspondence can be naturally determined from the side lengths of the triangle descriptors. The matched triangle pairs evaluate the appearance similarity between two point clouds, while the vertices' correspondence enables accurate estimation of their relative pose; both are crucial for the place recognition task. To enhance the accuracy of triangle matching, BTC introduces a binary descriptor, which describes the point distribution neighboring each keypoint. The local geometry information encoded by the binary descriptor augments descriptiveness and discriminativeness to the triangle descriptor. Collectively, the two descriptors achieve both global and local descriptions of the environment with high accuracy, efficiency, and robustness. We extensively compare the proposed BTC descriptor against state-of-the-art methods (e.g., Scan Context, LCD-Net) on a wide range of datasets collected using different types of LiDAR sensors (spinning LiDARs and non-repetitive scanning LiDARs) in various environments (urban, campus, forest, park, mountain). The quantitative results demonstrate that BTC exhibits greater adaptability and significant improvement in precision compared to its counterparts, especially in challenging cases with large viewpoint variations (e.g., reverse direction, large translation and/or rotation). To share our findings and contribute to the community, we open-source our code on GitHub:https://github.com/hku-mars/btc_descriptor.

In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, we first design an algorithm to efficiently extract local key points from the 3D point cloud and encode these key points into triangular descriptors. Then, place recognition is achieved by matching the side lengths (and some other information) of the descriptors between point clouds. The point correspondence obtained from the descriptor matching pair can be further used in geometric verification, which greatly improves the accuracy of place recognition. In our experiments, we extensively compare our proposed system against other state-of-the-art systems (i.e., M2DP, Scan Context) on public datasets (i.e., KITTI, NCLT, and Complex-Urban) and our self-collected dataset (with a non-repetitive scanning solid-state LiDAR). All the quantitative results show that STD has stronger adaptability and a great improvement in precision over its counterparts. To share our findings and make contributions to the community, we open source our code on our GitHub: github.com/hku-mars/STD.

In this article, we propose a novel light detection and ranging (LiDAR)(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real time. This proposed framework termed immediately meshing (ImMesh) comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module first utilizes the preprocessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The primary contribution of this work is the meshing module, which represents a scene by an efficient voxel structure, performs fast finding of voxels observed by new scans, and incrementally reconstructs triangle facets in each voxel. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3-D points to a 2-D local plane contained in the voxel, and then executes the meshing operation with pull, commit, and push steps for incremental reconstruction of triangle facets. To the best of authors' knowledge, this is the first work in the literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration.

Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at outdoor large scale scenes. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars’ unique features. Based on the unique scan pattern of Livox lidars, we propose an automatic lidar-camera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more accurate mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub1.

Frequency‐hopped M‐ary frequency‐shift‐keyed (FH‐MFSK) has the ability to handle with long multipath, large Doppler spreads, high ambient noise and rapid phase fluctuation, and is proved to be a robust modulation scheme in mobile shallow underwater acoustic channels. Aiming at this topic, a convolutionally encoded FH‐MFSK modulation scheme with a novel iterative multi‐channel reception is proposed. To obtain the novel reception suitable for non‐coherent M‐ary frequency‐shift‐keyed, the ideas of iterative multi‐channel demodulation and decoding are combined and extended. A modified multi‐channel soft‐input–soft‐output demodulator based on maximum‐a‐posterior criterion is derived, and substantial gains of several decibels in power efficiency are achieved. Extra gain can be obtained by utilising time diversity with long multipath delay. Simulation shows that a non‐coherent receiver equipped with five arrays obtains around 5.5 dB gain over the single non‐iterative receiver. A shallow water field testing at Songhua Lake with 300 m distance confirms that the proposed receiver verifies its robustness and usability.