Adverse weather conditions, low-light environments, and bumpy road surfaces pose significant challenges to SLAM in robotic navigation and autonomous driving. Existing datasets in this field predominantly rely on single sensors or combinations of LiDAR, cameras, and IMUs. However, 4D millimeter-wave radar demonstrates robustness in adverse weather, infrared cameras excel in capturing details under low-light conditions, and depth images provide richer spatial information. Multi-sensor fusion methods also show potential for better adaptation to bumpy roads. Despite some SLAM studies incorporating these sensors and conditions, there remains a lack of comprehensive datasets addressing low-light environments and bumpy road conditions, or featuring a sufficiently diverse range of sensor data. In this study, we introduce a multi-sensor dataset covering challenging scenarios such as snowy weather, rainy weather, nighttime conditions, speed bumps, and rough terrains. The dataset includes rarely utilized sensors for extreme conditions, such as 4D millimeter-wave radar, infrared cameras, and depth cameras, alongside 3D LiDAR, RGB cameras, GPS, and IMU. It supports both autonomous driving and ground robot applications and provides reliable GPS/INS ground truth data, covering structured and semi-structured terrains. We evaluated various SLAM algorithms using this dataset, including RGB images, infrared images, depth images, LiDAR, and 4D millimeter-wave radar. The dataset spans a total of 18.5 km, 69 minutes, and approximately 660 GB, offering a valuable resource for advancing SLAM research under complex and extreme conditions. Our dataset is available at https://gongweisheng.github.io/DIDLM.github.io/

This paper proposes an enhancement to the ORB-SLAM3 algorithm, tailored for applications on rugged road surfaces. Our improved algorithm adeptly combines feature point matching with optical flow methods, capitalizing on the high robustness of optical flow in complex terrains and the high precision of feature points on smooth surfaces. By refining the inter-frame matching logic of ORB-SLAM3, we have addressed the issue of frame matching loss on uneven roads. To prevent a decrease in accuracy, an adaptive matching mechanism has been incorporated, which increases the reliance on optical flow points during periods of high vibration, thereby effectively maintaining SLAM precision. Furthermore, due to the scarcity of multi-sensor datasets suitable for environments with bumpy roads or speed bumps, we have collected LiDAR and camera data from such settings. Our enhanced algorithm, ORB-SLAM3AB, was then benchmarked against several advanced open-source SLAM algorithms that rely solely on laser or visual data. Through the analysis of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) metrics, our results demonstrate that ORB-SLAM3AB achieves superior robustness and accuracy on rugged road surfaces.

Acting as the dynamic base stations, the unmanned aerial vehicles has a significant advantage over the conventional base station for target localization. When localizing a target, UAV can move toward the estimated target location after each estimation, making the received signals stronger after each iteration, and hence yield more localization precision for each iteration. In this paper, we propose target localization by UAV with radio maps as prior knowledge, which are generated from real topographic maps. We derived the minimum mean square error (MMSE) estimator for target localization by UAV with the radio map. Numerical results show that with radio map knowledge and the MMSE estimator, localization precision can be considerably improved.

In this paper, we aim to implement outdoor-to-indoor communication with the aid of an active reconfigurable intelligent surface (active-RIS), where the active-RIS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by indoor users (UEs) after shifting phase and magnifying amplitude. Then, the problem of joint beamforming optimization of the BS and active-ITS is investigated to maximize the system capacity. A computationally efficient algorithm is developed to solve it with suboptimal solutions. Simulations indicate the superiority of the active-RIS and the effectiveness of the proposed algorithm.