Visual Simultaneous Localization and Mapping (VSLAM) has developed as the basic ability of robots in past few decades. There are a lot of open-sourced and impressive SLAM systems. However, the majority of the theories and approaches of SLAM systems at present are based on the static scene assumption, which is usually not practical in reality because moving objects are ubiquitous and inevitable under most circumstances. In this paper the DDL-SLAM (Dynamic Deep Learning SLAM) is proposed, a robust RGB-D SLAM system for dynamic scenarios that, based on ORB-SLAM2, adds the abilities of dynamic object segmentation and background inpainting.We are able to detect moving objects utilizing both semantic segmentation and multi-view geometry. Having a static scene map allows inpainting background of the frame which has been obscured by moving objects, therefore the localization accuracy is greatly improved in the dynamic environment. Experiment with a public RGB-D benchmark dataset, the results clarify that DDL-SLAM can significantly enhance the robustness and stability of the RGB-D SLAM system in the highly-dynamic environment.

Existing video captioning methods usually ignore the important fine-grained semantic attributes, the video diversity, as well as the association and motion state between objects within and between frames. Thus, they cannot adapt to small sample data sets. To solve the above problems, this paper proposes a novel video captioning model and an adversarial reinforcement learning strategy. Firstly, an object-scene relational graph model is designed based on the object detector and scene segmenter to express the association features. The graph is encoded by the graph neural network to enrich the expression of visual features. Meanwhile, a trajectory-based feature representation model is designed to replace the previous data-driven method to extract motion and attribute information, so as to analyze the object motion in the time domain and establish the connection between the visual content and language under small data sets. Finally, an adversarial reinforcement learning strategy and a multi- branch discriminator are designed to learn the relationship between the visual content and corresponding words so that rich language knowledge is integrated into the model. Experimental results on three standard datasets and one small sample dataset indicate that our proposed method achieves state-of-the-art performance. Also, the ablation experiments and visualization results verify the effectiveness of proposed each strategy.

Multispectral pedestrian detection based on deep learning can provide a robust and accurate detection under different illumination conditions, which has important research significance in safety. In order to reduce the log-average miss rate of the object under different illumination conditions, a new one-stage detector suitable for multispectral pedestrian detection is proposed. First, in order to realize the complementarity between the information flows of the two modalities in the feature extraction stage to reduce the object loss, a low-cost cross-modality feature complementary module (CFCM) is proposed. Second, in order to suppress the background noise in different environments and enhance the semantic information and location information of the object, so as to reduce the error detection of the object, an attention-based feature enhancement fusion module (AFEFM) is proposed. Thirdly, through the feature complementarity of color-thermal image pair and the multi-scale fusion of depth feature layer, the horizontal and vertical multi-dimensional data mining of parallel deep neural network is realized, which provides effective data support for object detection algorithm. Finally, through the reasonable arrangement of proposed modules, a robust multispectral detection framework is proposed. The experimental results on the Korea Advanced Institute of Science and Technology (KAIST) pedestrian benchmark show that the proposed method has the lowest log-average miss rate compared with other state-of-the-art multispectral pedestrian detectors, and has a good balance in speed and accuracy.