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.

Summary The vast technological developments make data transmission more frequent over networks. So, the data needs to be secured and for that reason, there is a requirement to develop an effective encryption model. This article proposes a novel image encryption model using multi‐chaotic maps and blockchain (MCBE). Chaotic maps have been used in encryption models as chaotic maps have the properties of randomness, and non‐periodicity that get utilized in improving the efficiency of an encryption model. Logistic and tent maps have been employed as multi‐chaotic maps to make the encryption process effective in this work. A logistic map has a simple structure and is easily implementable but has some drawbacks like small keyspace. Keyspace and randomness are enhanced by using a tent map along with a logistic map. The proposed MCBE methodology applies to grayscale or colored images having different file formats and sizes. Initially, in the confusion phase, the permutation of the original image is performed based on a random permutation that changes the original image pixel's position. The encryption model applies multi‐chaotic maps, row‐wise and column‐wise in the diffusion phase to promote the efficiency of the encryption model. Finally, blockchain has been implemented using the SHA‐256 hash function to obtain the encrypted image that enhances the security by increasing the keyspace, to resist brute force attacks. The evaluation performance of the MCBE has been analyzed against various attacks namely statistical attacks, differential attacks, NIST randomness test, noise, and cropping attacks. The evaluated keyspace is which has high key sensitivity with a correlation of encrypted images close to 0. Maximum achieved entropy value of 7.9998, SSIM value between original and encrypted images nearby 0 confirms the dissimilarity. The Number of Pixels Change Rate (NPCR) value of 99.62%, and the value of Unified Average Changed Intensity (UACI) value of 33.54% are within the specified standard range. The calculated correlation coefficient, NPCR, and UACI values have been compared with the existing algorithms, and the results show that the proposed MCBE methodology has better performance than the other state‐of‐the‐art methods. The experimental results indicate that the chaotic ranges generated by multi‐chaotic maps and the SHA‐256 hash function improve the keyspace and security of the encryption model confirming the efficacy of the proposed model MCBE.

Analysis of Confocal Laser Scanning Microscopy (CLSM) images is gaining popularity in developmental biology for understanding growth dynamics. The automated analysis of such images is highly desirable for efficiency and accuracy. The first step in this process is segmentation and tracking leading to computation of cell lineages. In this paper, we present efficient, accurate, and robust segmentation and tracking algorithms for cells and detection of cell divisions in a 4D spatio-temporal image stack of a growing plant meristem. We show how to optimally choose the parameters in the watershed algorithm for high quality segmentation results. This yields high quality tracking results using cell correspondence evaluation functions. We show segmentation and tracking results on Confocal laser scanning microscopy data captured for 72 hours at every 3 hour intervals. Compared to recent results in this area, the proposed algorithms provide significantly longer cell lineages and more comprehensive identification of cell divisions.