CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR

We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time

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

ABSTRACT FACE REPRESENTATION AND TRACKING USING GABOR WAVELET NETWORKS

This work presents a new method for a human face representation and tracking in video sequences. A discrete face template is represented by linear combination of the continuous 2D odd-Gabor wavelet functions (Gabor Wavelet Network). The weights and 2D parameters (position, scale and orientation) of each wavelet are determined optimally. Using this representation, an effective face tracking method is achieved that is robust to illumination changes and deformations of the face image such as eye blinking and smile.

Teleoperating Assistive Robots: A Novel User Interface Relying on Semi-Autonomy and 3D Environment Mapping

Despite remarkable progress of service robotics in recent years, it seems that a fully autonomous robot which would be able to solve everyday household tasks in a safe and reliable manner is still unachievable. Under certain circumstances, a robot’s abilities might be supported by a remote operator. In order to allow such support, we present a user interface for a semi-autonomous assistive robot allowing a non-expert user to quickly asses the situation on a remote site and carry out subtasks which cannot be finished automatically. The user interface is based on a mixed reality 3D environment and fused sensor data, which provides a high level of situational and spatial awareness for teleoperation as well as for telemanipulation. Robot control is based on low-cost commodity hardware, optionally including a 3D mouse and stereoscopic display. The user interface was developed in a human-centered design process and continuously improved based on the results of five evaluations with a total of 81 novice users

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use.Fil: Li, Jianning. Technische Universitat Graz; AustriaFil: Pimentel, Pedro. No especifíca;Fil: Szengel, Angelika. No especifíca;Fil: Ehlke, Moritz. No especifíca;Fil: Lamecker, Hans. No especifíca;Fil: Zachow, Stefan. No especifíca;Fil: Estacio, Laura. Universidad Católica San Pablo; PerúFil: Doenitz, Christian. No especifíca;Fil: Ramm, Heiko. No especifíca;Fil: Shi, Haochen. Shanghai Jiao Tong University; ChinaFil: Chen, Xiaojun. Shanghai Jiao Tong University; ChinaFil: Matzkin, Victor Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Newcombe, Virginia. University of Cambridge; Estados UnidosFil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Jin, Yuan. Technische Universitat Graz; AustriaFil: Ellis, David G.. No especifíca;Fil: Aizenberg, Michele R.. University of Nebraska; Estados UnidosFil: Kodym, Oldrich. No especifíca;Fil: Spanel, Michal. No especifíca;Fil: Herout, Adam. No especifíca;Fil: Mainprize, James G.. Sunnybrook Health Sciences Centre; CanadáFil: Fishman, Zachary. Sunnybrook Health Sciences Centre; CanadáFil: Hardisty, Michael R.. Sunnybrook Health Sciences Centre; CanadáFil: Bayat, Amirhossein. No especifíca;Fil: Shit, Suprosanna. No especifíca;Fil: Wang, Bomin. Shandong University; ChinaFil: Liu, Zhi. Shandong University; ChinaFil: Eder, Matthias. Technische Universitat Graz; AustriaFil: Pepe, Antonio. Technische Universitat Graz; AustriaFil: Gsaxner, Christina. Technische Universitat Graz; AustriaFil: Alves, Victor. Universidade do Minho; PortugalFil: Zefferer, Ulrike. Medizinische Universität Graz; AustriaFil: Von Campe, Gord. Medizinische Universität Graz; AustriaFil: Pistracher, Karin. Medizinische Universität Graz; AustriaFil: Schafer, Ute. Medizinische Universität Graz; AustriaFil: Schmalstieg, Dieter. Technische Universitat Graz; AustriaFil: Menze, Bjoern H.. No especifíca;Fil: Glocker, Ben. Imperial College London; Reino UnidoFil: Egger, Jan. Computer Algorithms For Medicine Laboratory; Austri