Visual place recognition is a challenging problem in robotics and autonomous systems because the scene undergoes appearance and viewpoint changes in a changing world. Existing state-of-the-art methods heavily rely on CNN-based architectures. However, CNN cannot effectively model image spatial structure information due to the inherent locality. To address this issue, this paper proposes a novel Transformer-based place recognition method to combine local details, spatial context, and semantic information for image feature embedding. Firstly, to overcome the inherent locality of the convolutional neural network (CNN), a hybrid CNN-Transformer feature extraction network is introduced. The network utilizes the feature pyramid based on CNN to obtain the detailed visual understanding, while using the vision Transformer to model image contextual information and aggregate task-related features dynamically. Specifically, the multi-level output tokens from the Transformer are fed into a single Transformer encoder block to fuse multi-scale spatial information. Secondly, to acquire the multi-scale semantic information, a global semantic NetVLAD aggregation strategy is constructed. This strategy employs semantic enhanced NetVLAD, imposing prior knowledge on the terms of the Vector of Locally Aggregated Descriptors (VLAD), to aggregate multi-level token maps, and further concatenates the multi-level semantic features globally. Finally, to alleviate the disadvantage that the fixed margin of triplet loss leads to the suboptimal convergence, an adaptive triplet loss with dynamic margin is proposed. Extensive experiments on public datasets show that the learned features are robust to appearance and viewpoint changes and achieve promising performance compared to state-of-the-arts.

In order to achieve the high motion precision of the macro-micro parallel manipulator system for a large radio telescope, its real-time motion planning is dealt with in this paper. To uniquely determine the spatial position and orientation of the cable driven parallel manipulator, the real-time optimization is conducted according to the principle of uniform tension in the six driving cables. Synchronized points and the “judge and wait” mechanism ensure the continuity and synchrony of the trajectory tracking of the two parallel manipulators. The preadjustment of the micro parallel manipulator reduces the drastic dynamical coupling as a result of the fast movement of the micro manipulator. Besides, over-workspace avoidance procedure is introduced to restrict the micro parallel manipulator not to exceed it workspace. Experimental results validate the high precision of the macro-micro parallel manipulator system with supervisory controller and the motion prediction algorithm.