CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data

This paper presents a novel method for ground segmentation in Velodyne point clouds. We propose an encoding of sparse 3D data from the Velodyne sensor suitable for training a convolutional neural network (CNN). This general purpose approach is used for segmentation of the sparse point cloud into ground and non-ground points. The LiDAR data are represented as a multi-channel 2D signal where the horizontal axis corresponds to the rotation angle and the vertical axis the indexes channels (i.e. laser beams). Multiple topologies of relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and evaluated using a manually annotated dataset we prepared. The results show significant improvement of performance over the state-of-the-art method by Zhang et al. in terms of speed and also minor improvements in terms of accuracy.Comment: ICRA 2018 submissio

Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam

Analysis and automatic annotation of singer's postures during concert and rehearsal

Bodily movement of music performers is widely acknowledged to be a means of communication with the audience. For singers, where the necessity of movement for sound production is limited, postures, i.e. static positions of the body, may be relevant in addition to actual movements. In this study, we present the results of an analysis of a singer’s postures, focusing on differences in postures between a dress rehearsal without audience and a concert with audience. We provide an analysis based on manual annotation of postures and propose and evaluate methods for automatic annotation of postures based on motion sensing data, showing that automatic annotation is a viable alternative to manual annotation. Results furthermore suggest that the presence of an audience leads the singer to use more ‘open’ postures, and differentiate more between different postures. Also, speed differences of transitions from one posture to another are more pronounced in concert than during rehearsal