Abstract-We present a method of utilizing depth information as provided by RGBD sensors for robust real-time visual simultaneous localisation and mapping (SLAM) by augmenting monocular visual SLAM to take into account depth data. This is implemented based on the feely available software "Parallel Tracking and Mapping" by Georg Klein. Our modifications allow PTAM to be used as a 6D visual SLAM system even without any additional information about odometry or from an inertial measurement unit.
3D Sensors are used for many different applications, e.g. scene reconstruction, object detection and mobile robots, etc. Several studies on usability and accuracy have been done for different sensors. However, all these studies have used different settings for the different sensors. For this reason we compare five 3D sensors, including the structured light sensors Microsoft Kinect and ASUS Xtion Pro Live and the time of flight sensors Fotonic E70P, IFM O3D200 and Nippon Signal FX6, using the same settings. The sensor noise, absolute error and point detection rates are compared for different depth values, environmental illumination and different surfaces. Also simple models of the noise depending on the measured depth are proposed. It is found that the structured light sensors are very accurate for close ranges. The time of flight sensors have more noise, but the noise does not increase as strongly with the measured distance. Further, it is found that these sensors can be used for outdoor applications.
Depth cameras, like the Microsoft Kinect system, are valuable sensors for mobile robotics since their data enables a highly detailed perception of the environmental structure. Certainly, their amount of data is often too high to be processed in real-time by the limited resources of mobile robots. One way of using these sensors is to reduce the amount of data by extracting features like planes from the raw depth images. In this work we present a method to extract planes from depth images based on the Randomized Hough Transformation, which is specially adapted to the properties of the Kinect sensor. Therefore we use a noise model of the sensor to solve the task of finding proper parameter metrics for the Randomized Hough Transform. As a result, our approach extracts the planes from a depth image in less than one millisecond on the platform of a mobile robot and is therefore real-time capable.
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