We propose a dynamic simultaneous localization and mapping technology for unsupervised motion removal (UMR-SLAM), which is a deep learning-based dynamic RGBD SLAM. It is the first time that a scheme combining scene flow and deep learning SLAM is proposed to improve the accuracy of SLAM in dynamic scenes, in response to the situation where dynamic objects cause pose changes. The entire process does not require explicit object segmentation as supervisory information. We also propose a loop detection scheme that combines optical flow and feature similarity in the backend optimization section of the SLAM system to improve the accuracy of loop detection. UMR-SLAM is rewritten based on the DROID-SLAM code architecture. Through experiments on different datasets, it has been proven that our scheme has higher pose accuracy in dynamic scenarios compared with the current advanced SLAM algorithm.

A recent trend in machine learning is to use deep architectures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network-based model is designed and optimized, particularly for smart phone applications. Experiment results on all the NLP sub-tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requiring real-time response, highlighting the potential of the proposed solution for practical NLP systems.