In a typical Internet of Medical Things (IoMT) system, it is common for users’ physiological data, collected by medical sensors, to be processed and shared in a third-party environment, such as a cloud server. Medical data sharing in a third-party environment must guarantee data integrity and source authentication, and robust privacy-preserving measures must be taken to enhance reliability. Searchable encryption (SE) has been leveraged in the IoMT to address the issue of exposing private data during storage and transmission. However, existing works suffer from shortcomings, such as vulnerability to data tampering. In this paper, we propose a novel privacy-preserving data sharing scheme with identity authentication based on SE that addresses the heavyweight public key management problem within a Public Key Infrastructure (PKI) framework. In IB-BPSE, a redactable signature (RS) assisted by blockchain and Merkle tree structure ensures authentication and integrity. Searchable proxy re-encryption encrypts private data, enabling efficient ciphertext search and multi-user data sharing. In addition, the scheme is provably secure in the random oracle model, and the performance analysis demonstrates that the system achieves ideal comprehensive performance. Finally, we propose an application scenario in IoMT based on IB-BPSE.

This paper addresses the challenges of registering two rigid semantic scene graphs, an essential capability when an autonomous agent needs to register its map against a remote agent, or against a prior map. The hand-crafted descriptors in classical semantic-aided registration, or the ground-truth annotation reliance in learning-based scene graph registration, impede their application in practical real-world environments. To address the challenges, we design a scene graph network to encode multiple modalities of semantic nodes: open-set semantic feature, local topology with spatial awareness, and shape feature. These modalities are fused to create compact semantic node features. The matching layers then search for correspondences in a coarse-to-fine manner. In the back-end, we employ a robust pose estimator to decide transformation according to the correspondences. We manage to maintain a sparse and hierarchical scene representation. Our approach demands fewer GPU resources and fewer communication bandwidth in multi-agent tasks. Moreover, we design a new data generation approach using vision foundation models and a semantic mapping module to reconstruct semantic scene graphs. It differs significantly from previous works, which rely on ground-truth semantic annotations to generate data. We validate our method in a two-agent SLAM benchmark. It significantly outperforms the hand-crafted baseline in terms of registration success rate. Compared to visual loop closure networks, our method achieves a slightly higher registration recall while requiring only 52 KB of communication bandwidth for each query frame. Code available at:http://github.com/HKUST-Aerial-Robotics/SG-Reg

Multirotors are usually desired to enter confined narrow tunnels that are barely accessible to humans in various applications including inspection, search and rescue, and so on. This task is extremely challenging since the lack of geometric features and illuminations, together with the limited field of view, cause problems in perception; the restricted space and significant ego airflow disturbances induce control issues. This article introduces an autonomous aerial system designed for navigation through tunnels as narrow as 0.5 m in diameter. The real-time and online system includes a virtual omni-directional perception module tailored for the mission and a novel motion planner that incorporates perception and ego airflow disturbance factors modeled using camera projections and computational fluid dynamics analyses, respectively. Extensive flight experiments on a custom-designed quadrotor are conducted in multiple realistic narrow tunnels to validate the superior performance of the system, even over human pilots, proving its potential for real applications. In addition, a deployment pipeline on other multirotor platforms is outlined and open-source packages are provided for future developments.

Adopting omnidirectional Field of View (FoV) cameras in aerial robots vastly improves perception ability, significantly advancing aerial robotics’s capabilities in inspection, reconstruction, and rescue tasks. However, such sensors also elevate system complexity, e.g., hardware design, and corresponding algorithm, which limits researchers from utilizing aerial robots with omnidirectional FoV in their research. To bridge this gap, we propose OmniNxt, a fully open-source aerial robotics platform with omnidirectional perception. We design a high-performance flight controller Nxt-FC and a multi-fisheye camera set for OmniNxt. Meanwhile, the compatible software is carefully devised, which empowers OmniNxt to achieve accurate localization and real-time dense mapping with limited computation resource occupancy. We conducted extensive real-world experiments to validate the superior performance of OmniNxt in practical applications. All the hardware and software are open-access at3, and we provide docker images of each crucial module in the proposed system. Project page: https://hkust-aerial-robotics.github.io/OmniNxt.

Collaborative simultaneous localization and mapping (CSLAM) is essential for autonomous aerial swarms, laying the foundation for downstream algorithms, such as planning and control. To address existing CSLAM systems' limitations in relative localization accuracy, crucial for close-range UAV collaboration, this article introduces$D^{2}$SLAM—a novel decentralized and distributed CSLAM system.$D^{2}$SLAM innovatively manages near-field estimation for precise relative state estimation in proximity and far-field estimation for consistent global trajectories. Its adaptable front-end supports both stereo and omnidirectional cameras, catering to various operational needs and overcoming field-of-view challenges in aerial swarms. Experiments demonstrate$D^{2}$SLAM's effectiveness in accurate ego-motion estimation, relative localization, and global consistency. Enhanced by distributed optimization algorithms,$D^{2}$SLAM exhibits remarkable scalability and resilience to network delays, making it well suited for a wide range of real-world aerial swarm applications. We believe the adaptability and proven performance of$D^{2}$SLAM signify a notable advancement in autonomous aerial swarm technology.