This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard’s patterns. Powell’s method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D-2D matching problem. The corresponding 3D-2D points are used to calculate the absolute pose of the two sensors with Unified Perspective-n-Point (UPnP). Further, the calculated parameters are regarded as initial values and are refined using the Levenberg-Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR and a Ladybug3 camera under the proposed re-projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters.

Understandings of human activity through wearable sensors will enable the next-generation human-oriented computing. However, most of researches on the activity recognition so far are based on small number of test subjects, and not well adapted for real world applications. To overcome the situation, we have started a project named "HASC Challenge" to collect a large scale human activity corpus. By the end of 2010, by the collaboration of 20 teams, more than 6700 accelerometer data with 540 subjects have been collected through our project. We also developed a tool named "HASC Tool" for management, evaluation and collection of the large number of activity sensor data.

In this paper, we propose a high accuracy indoor positioning method that uses residual magnetism in addition to Pedestrian Dead Reckoning (PDR) and WiFi-based localization methods. Our proposed method needs WiFi and magnetic field fingerprints, which are created by measuring in advance the WiFi radio waves and the magnetic field in the target map. The fingerprints are represented by a Gaussian Mixture Models (GMMs) to reduce the amount of computation. Our proposed method estimates positions by comparing the pedestrian sensor and fingerprint values by particle filters. We evaluated this method in real environments and confirmed that it provides accurate indoor positioning with a mean error less than 8 m and more accurate position detection than existing techniques.

Human activity recognition through the wearable sensor will enable a next-generation human-oriented biquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.UbiComp \u2711 Proceedings of the 13th international conference on Ubiquitous computing, September 17-21, 2011, Beijing, Chin

Human activity recognition by wearable sensors will enable a next-generation human-oriented ubiquitous computing. However, most of the existing research on human activity recognition is based on a small number of subjects, and lab-created-data. To overcome this problem, we hold HASC Challenge as a technical challenge to collect the data for activity recognition. In addition to HASC Challenge, we collected indoor pedestrian sensing data of 107 people with a balance of gender and age (HASC-IPSC). Through these data collection, we gathered 111,968 sensor files of 510 subjects. For the convenience of the future researchers in this field, we combined them as a single corpus named HASC-PAC2016 and make it public. Baseline recognition result of HASC-PAC2016 segmented data is 73.4% accuracy for overall, 81.4% for limited by terminal position, and 85.1% with file-based recognition. For sequence data, we only get 73.4% even for limited subjects. This shows we need further research of activity recognition using HASC-PAC2016.