The explosive growth of remote-sensing images generated by emerging space–air–ground integrated IoT networks makes centralized detector training infeasible due to limited bandwidth and strict data privacy constraints. While lightweight single-stage object detectors offer efficiency, they suffer significant accuracy degradation for small, dense, and arbitrarily oriented targets. Furthermore, existing federated object detection frameworks typically neglect client heterogeneity. To overcome these limitations, we propose a two-stage personalized federated detection framework. In Stage 1, we independently train a conventional single-stage rotated object detector on each client and aggregate model updates using an adaptive similarity momentum aggregation (ASMA) strategy, effectively pooling knowledge across non-IID client datasets to improve global generalization. In Stage 2, each client is equipped with a private selective depthwise attention convolution (SDAConv) module, leveraging Stage-1 priors to reconstruct fine-grained, client-specific features without additional communication overhead, thus tailoring predictions to local data distributions. Experiments conducted on five non-IID splits derived from DOTA-1.0, along with DIOR and VisDrone datasets, demonstrate improvements of up to +3.5 mAP compared to federated learning baselines under the same communication budget, simultaneously maintaining global robustness and enhancing local detection accuracy.

Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The dataset contains 1553 images from 93 indoor locations. Various appearance changes between images of the same location are included and can help the model to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization. The RaPNet code and dataset are available at https://github.com/ivipsourcecode/RaP-Net.

Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may be significantly different in some successive days. Such out-of-sight dynamics pose great challenges to the robustness of pose estimation, and hence a robot’s long-term deployment and operation. To differentiate the forementioned problem from the conventional works which are usually evaluated in a static setting in a single run, the term lifelong SLAM is used here to address SLAM problems in an ever-changing environment over a long period of time. To accelerate lifelong SLAM research, we release the OpenLORIS-Scene datasets. The data are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life. We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately. The datasets and benchmark are available online at lifelong-robotic-vision.github.io/dataset/scene.

A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for robot autonomy. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments. This paper shows that feature extraction with deep convolutional neural networks (CNNs) can be seamlessly incorporated into a modern SLAM framework. The proposed SLAM system utilizes a state-of-the-art CNN to detect keypoints in each image frame, and to give not only keypoint descriptors, but also a global descriptor of the whole image. These local and global features are then used by different SLAM modules, resulting in much more robustness against environmental changes and viewpoint changes compared with using hand-crafted features. We also train a visual vocabulary of local features with a Bag of Words (BoW) method. Based on the local features, global features, and the vocabulary, a highly reliable loop closure detection method is built. Experimental results show that all the proposed modules significantly outperforms the baseline, and the full system achieves much lower trajectory errors and much higher correct rates on all evaluated data. Furthermore, by optimizing the CNN with Intel OpenVINO toolkit and utilizing the Fast BoW library, the system benefits greatly from the SIMD (single-instruction-multiple-data) techniques in modern CPUs. The full system can run in real-time without any GPU or other accelerators. The code is public at https://github.com/ivipsourcecode/dxslam.

In this paper, we consider multiple‐input multiple‐output (MIMO) multi‐tone code division multiple access (MT‐CDMA) uplink transmission over multipath fading channels. The zero‐forcing vertical Bell Laboratories layered space‐time architecture (ZF V‐BLAST) algorithm and maximum ratio combining scheme are applied at the receiver. The average bit error rate (BER) expression is derived provided that the number of receive antennas is not less than that of transmit antennas. The BER expression is verified by simulations. Numerical results show that the numbers of transmit and receive antennas have significant effects on the BER performance of the considered system. Spatial and path diversity show different capabilities to improve the BER performance. The MIMO MT‐CDMA system based on the ZF V‐BLAST algorithm is capable of achieving a better BER performance and a higher capacity than the conventional MT‐CDMA system. Copyright © 2011 John Wiley & Sons, Ltd.

Heterogeneous Networks (HetNets) has attracted considerable attention since they can improve the system capacity and user throughput in Long Term Evolution (LTE)-Advanced system. However, due to the different coverage sizes for macro eNodeB (eNB) and pico eNB, the handover performance of UE will be impacted especially when the user equipment (UE) moves in medium or high speed in HetNets. Three metrics are agreed in 3GPP RAN WG2 to evaluate the handover performance: radio link failure (RLF) rate, handover failure (HOF) rate, and short time of stay (short ToS). In this paper, firstly, based on the three metrics, the handover performance in HetNets is analyzed. Then, we propose two schemes to solve the issue of handover performance deterioration when UE moves in medium speed. One scheme is to optimize pico-macro handover, and the other is to optimize macro-pico handover. In other words, two schemes can separately optimize pico cell leaving and attaching. Furthermore, the two schemes can be used jointly to further improve the mobility robustness in HetNets. System level simulation is employed to present the handover performance improvement.