Light detection and ranging (Lidar) has become the core device in simultaneous localization and mapping (SLAM) system, which has attracted wide attentions in recent years. However, the point clouds acquired by Lidar are sparse and mutually uncorrelated, making it difficult to calculate correspondences between consecutive frames and result in data drifting. In this situation, we propose LILO (a tightly coupled Lidar + IMU fusion system with loop optimization) to address the above problems. Firstly, the ground Lidar points are segmented and removed. Secondly, under our framework, IMU (Inertial Measurement Unit) is used to de-skew the motion distortion of Lidar. Thirdly, a voxel grid filtering process is implemented to further eliminate the redundant points, and feature matching is performed by identifying lines and planes. Finally, loop closure detection is realized to correct pose estimation and localization error. Experimental results on both public dataset and field test demonstrate the effectiveness of our algorithm.

As a sub-field of video content analysis, action recognition has received extensive attention in recent years, which aims to recognize human actions in videos. Compared with a single image, video has a temporal dimension. Therefore, it is of great significance to extract the spatio-temporal information from videos for action recognition. In this paper, an efficient network to extract spatio-temporal information with relatively low computational load (dubbed MEST) is proposed. Firstly, a motion encoder to capture short-term motion cues between consecutive frames is developed, followed by a channel-wise spatio-temporal module to model long-term feature information. Moreover, the weight standardization method is applied to the convolution layers followed by batch normalization layers to expedite the training process and facilitate convergence. Experiments are conducted on five public datasets of action recognition, Something-Something-V1 and -V2, Jester, UCF101 and HMDB51, where MEST exhibits competitive performance compared to other popular methods. The results demonstrate the effectiveness of our network in terms of accuracy, computational cost and network scales.