To enable autonomous mobile robots to navigate safely and efficiently in crowded environments, an effective way is to predict future occupancy states of their local surroundings to navigate proactively. However, most prior works focus on occupancy grid map (OGM) prediction for automous driving scenarios and typically generates deterministic single-mode predictions. To address these limitations, we present a diffusion model-based framework for stochastic and uncertainty-aware OGM prediction. Prior OGM predictors in crowded environment settings are limited to binary occupancy representations (occupied or free), which constrain their expressive power and ignore even basic semantic distinctions such as humans, static obstacles, and free space. Our framework extends traditional OGM predictions to include these fundamental semantic categories, enhancing environmental prediction, and facilitating downstream tasks. Additionally, we develop a Unity-based 3D pedestrian-robot simulator that supports teleoperation-enabled acquisition of automatically annotated autonomous mobile robot data. Experimental evaluations demonstrate that our framework achieves higher prediction accuracy on binary OGM prediction tasks across three publicly available datasets, while achieving promise results in semantic OGM prediction tasks using datasets collected from our simulation environments. Implementation and simulator are open source at https://github.com/daleigehhh/DiffOGMP.

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements.

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications.