Portable 3D laser-camera calibration system with color fusion for SLAM

Nowadays, the use of RGB-D sensors have focused a lot of research in computer vision and robotics. These kinds of sensors, like Kinect, allow to obtain 3D data together with color information. However, their working range is limited to less than 10 meters, making them useless in some robotics applications, like outdoor mapping. In these environments, 3D lasers, working in ranges of 20-80 meters, are better. But 3D lasers do not usually provide color information. A simple 2D camera can be used to provide color information to the point cloud, but a calibration process between camera and laser must be done. In this paper we present a portable calibration system to calibrate any traditional camera with a 3D laser in order to assign color information to the 3D points obtained. Thus, we can use laser precision and simultaneously make use of color information. Unlike other techniques that make use of a three-dimensional body of known dimensions in the calibration process, this system is highly portable because it makes use of small catadioptrics that can be placed in a simple manner in the environment. We use our calibration system in a 3D mapping system, including Simultaneous Location and Mapping (SLAM), in order to get a 3D colored map which can be used in different tasks. We show that an additional problem arises: 2D cameras information is different when lighting conditions change. So when we merge 3D point clouds from two different views, several points in a given neighborhood could have different color information. A new method for color fusion is presented, obtaining correct colored maps. The system will be tested by applying it to 3D reconstruction.

Portable 3D laser-camera calibration system with color fusion for SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR2012), Taipei, Taiwan, Aug. 29-31, 2012.

A low cost system for the prediagnosis of the human tremor

Human tremor can be defined as a somewhat rhythmic and quick movement of one or more body parts. In some people, it is a symptom of a neurological disorder. From the mathematical point of view, human tremor can be defined as a weighted contribution of different sinusoidal signals which causes oscillations of some parts of the body. This sinusoidal is repeated over time, but its amplitude and frequency change slowly. This is why amplitude and frequency are considered important factors in the tremor characterization, and thus for its diagnosis. In this paper, a tool for the prediagnosis of the human tremor is presented. This tool uses a low cost device (<$40) and allows to compute the main factors of the human tremor accurately. Real cases have been tested using the algorithms developed in this investigation. The patients suffered from different tremor severities, and the components of amplitude and frequency were computed using a series of tests. These additional measures will help the experts to make better diagnoses allowing them to focus on specific stages of the test or get an overview of these tests. From the experimental, we stated that not all tests are valid for every patient to give a diagnosis. Guided by years of experience, the expert will decide which test or set of tests are the most appropriate for a patient.