While neural compression has the potential to revolutionize image compression, recent studies have emphasized its ability to introduce subtle artifacts that could alter the image content.Concerned about the impact of such modifications on scientific images, this work explores the potential effects of neural compression on these images, focusing on two critical aspects: semantic understanding and forensic integrity.We use scientific image datasets to assess the performance of neural compression techniques on Visual Question Answering (VQA) and copy-move forgery detection tasks.Our findings indicate that the subtle changes introduced by neural compression do not significantly degrade the performance of state-ofthe-art solutions.In the experiments, neurally compressed images sufficiently preserved the original semantics and forensic traces.Moreover, compared to lossy techniques, e.g., JPEG compression, at similar bit-per-pixel (bpp) rates, neural compression demonstrates a superior ability to preserve both semantic content and forensic traces, even at high compression levels.Our results suggest that neural compression may provide a viable alternative to lossy compression for scientific images.

Multiprocessor systems-on-chip (MPSoCs) based on 3-D networks-on-chip (3D-NoCs) are crucial architectures for robust parallel computing, efficiently sharing resources across complex applications. To ensure the secure operation of these systems, it is essential to implement adaptive, fault-tolerant mechanisms capable of protecting sensitive data. This work proposes the Securet3d routing algorithm, which establishes secure data paths in fault-tolerant 3D-NoCs. Our approach enhances the Reflect3d algorithm by introducing a detailed scheme for mapping secure paths and improving the system’s ability to withstand faults. To validate its effectiveness, we compare Securet3d with three other fault-tolerant routing algorithms for vertically-partially connected 3D-NoCs. All algorithms were implemented in SystemVerilog and evaluated through simulation using ModelSim and hardware synthesis with Cadence’s Genus tool. Experimental results show that Securet3d reduces latency and enhances cost-effectiveness compared with other approaches. When implemented with a 28-nm technology library, Securet3d demonstrates minimal area and energy overhead, indicating scalability and efficiency. Under denial-of-service (DoS) attacks, Securet3d maintains basically unaltered average packet latencies on 70, 90, and 29 clock cycles for uniform random, bit-complement, and shuffle traffic, significantly lower than those of other algorithms without including security mechanisms (5763, 4632, and 3712 clock cycles in average, respectively). These results highlight the superior security, scalability, and adaptability of Securet3d for complex communication systems.

Simultaneous localization and mapping (SLAM) is a fundamental task for numerous applications such as autonomous navigation and exploration. Despite many SLAM datasets have been released, current SLAM solutions still struggle to have sustained and resilient performance. One major issue is the absence of high-quality datasets including diverse all-weather conditions and a reliable metric for assessing robustness. This limitation significantly restricts the scalability and generalizability of SLAM technologies, impacting their development, validation, and deployment. To address this problem, we present SubT-MRS, an ex-tremely challenging real-world dataset designed to push SLAM towards all-weather environments to pursue the most robust SLAM performance. It contains multi-degraded en-vironments including over 30 diverse scenes such as structureless corridors, varying lighting conditions, and perceptual obscurants like smoke and dust; multimodal sensors such as LiDAR, fisheye camera, IMU, and thermal camera; and multiple locomotions like aerial, legged, and wheeled robots. We developed accuracy and robustness evaluation tracks for SLAM and introduced novel robustness metrics. Comprehensive studies are performed, revealing new obser-vations, challenges, and opportunities for future research.

This article surveys recent progress and discusses future opportunities for simultaneous localization and mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary exploration, search and rescue, disaster response, and automated mining, among others. SLAM in underground environments has recently received substantial attention, thanks to theDARPA Subterranean (SubT) Challenge, a global robotics competition aimed at assessing and pushing the state of the art in autonomous robotic exploration and mapping in complex underground environments. This article reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the article has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on light detection and ranging (LIDAR)-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multirobot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the “dirty details” behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, which are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts and constitute a useful resource for researchers and practitioners.

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, Visual-inertial odometry, and LiDAR-inertial odometry. The Visual-inertial odometry and LiDAR-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10% of the running time compared with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer’s effort to the DARPA Subterranean Challenge where the team won 1stand 2ndplace in the Tunnel and Urban Circuits1, respectively.