Simultaneous Localization and Mapping (SLAM) is considered to be an essential capability for intelligent vehicles and mobile robots. However, most of the current lidar SLAM approaches are based on the assumption of a static environment. Hence the localization in a dynamic environment with multiple moving objects is actually unreliable. The paper proposes a dynamic SLAM framework RF-LIO, building on LIO-SAM, which adds adaptive multi-resolution range images and uses tightly-coupled lidar inertial odometry to first remove moving objects, and then match lidar scan to the submap. Thus, it can obtain accurate poses even in high dynamic environments. The proposed RF-LIO is evaluated on both self-collected datasets and open Urbanloco datasets. The experimental results in high dynamic environments demonstrate that, compared with LOAM and LIO-SAM, the absolute trajectory accuracy of the proposed RF-LIO can be improved by 90% and 70%, respectively. RF-LIO is one of the state-of-the-art SLAM systems in high dynamic environments.

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This paper investigates a class of generalized Cohen–Grossberg neural networks (CGNNs) with discontinuous activations and mixed delays. Based on the nonsmooth analysis theory, the drive-response concept, differential inclusions theory, we give several basic assumptions to gain the finite-time synchronization issue of CGNNs. Sufficient conditions are provided without the boundedness or monotonicity of discontinuous activation functions. Moreover, one can estimate the settling time’s upper bounds of the system. At last, two numerical examples and their simulations are given to further show the benefits of the obtained control approach.

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