A method is presented for improving the localization accuracy of a mobile manipulator. The system consists of an industrial manipulator mounted on a mobile platform to perform fuselage drilling/riveting tasks in indoor environment. The localization is based on dual laser range finders mounted on the system and a number of artificial landmarks preset in the environment. Localization based on trilateration method is presented in this study. The adaptive unscented Kalman filter method is used to improve the accuracy of localization as well. The proposed method has been verified through experiments and the localization accuracy of mobile manipulator can be improved to 8 mm in most cases.

This paper describes the NiuTrans system for the WMT21 translation efficiency task 1 . Following last year's work, we explore various techniques to improve efficiency while maintaining translation quality. We investigate the combinations of lightweight Transformer architectures and knowledge distillation strategies. Also, we improve the translation efficiency with graph optimization, low precision, dynamic batching, and parallel pre/postprocessing. Our system can translate 247,000 words per second on an NVIDIA A100, being 3× faster than last year's system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. The code, model, and pipeline will be available at NiuTrans.NMT 2 .

No abstract