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With a wide range of applications in autonomous driving and robotics, semantic segmentation for large‐scale outdoor point clouds is a critical and challenging issue. Due to the large number and irregular arrangement of point clouds, it is difficult to balance the efficiency and effectiveness. In this paper, we propose LessNet, a lightweight and efficient voxel‐based method for LiDAR‐only semantic segmentation, taking advantage of cylindrical partition and intra‐voxel feature fusion. Specifically, we use a cylindrical partition method to distribute the outdoor point clouds more evenly in voxels. To better encode the voxel features, we adopt an intra‐voxel aggregation method without querying neighbours. The voxel features are further input into a lightweight and effective 3D U‐net to aggregate local features and dilate the receptive field. Extensive experiments prove the satisfied semantic segmentation performance and the improvement of each component in our proposed framework. Our method is capable of processing more than one million point clouds at a time while retaining low latency and few parameters. Moreover, our method achieves comparable performance with state‐of‐the‐art approaches and outperforms all projection‐based methods on the SemanticKITTI benchmark.

This paper presents a Multiple Unmanned Aerial Vehicles (UAVs) cooperative reconnaissance task allocation model based on heterogeneous target value and proposes an improved Multi-Verse Optimizer (MVO) algorithm. Firstly, according to the reconnaissance value of the target, the reconnaissance targets are divided into high-value targets, low-value targets and decoy targets. It improves the authenticity of the problem. The purpose of task allocation is to maximize the reconnaissance revenue of UAVs as much as possible under the condition of minimizing the reconnaissance time and fuel loss of UAVs to the targets. Then, to solve the model, this paper improves the traditional MVO algorithm. Adaptive compression factor is introduced to improve the convergence speed of the algorithm. In addition, the differential mutation operation is performed in the wormhole movement stage to enhance the global search ability of the algorithm. The simulation results show that the improved algorithm can successfully solve the reconnaissance task allocation problem under different target values, and has obvious advantages in reconnaissance revenue and calculation speed compared with other methods.INDEX TERMS Multi-UAVs, reconnaissance task allocation, heterogeneous target value, MVO algorithm.

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