The Q.bo robot has been experimenting with using the Xtion Pro to provide it with 3D vision.
The team at TheCorpora Robotic Company has added an ASUS's Xtion Pro Live sensor to the existing set of sensors for Q.bo, its cute soon-to-be-released robot. Q.bo already has two HD webcams and can be fitted with 4 ultrasound sensors (2 in the front, and 2 in the rear), but in an attempt to give it a 3D sensing capability, the team crafted an adapter for the small. lightweight ASUS sensor in a mount that fits over Q.bo's head.
The blog post announcing this innovation explains:
The ability of autonomous localization and simultaneous mapping are crucial for autonomous robots who need to adapt to their environments. In Robotics, this method is known as SLAM (Simultaneous Localization And Mapping) and it can be implemented in several algorithms relevant to 2D or 3D environments by using different types of input sensors (laser, sonars, odometry, webcams, etc.).
[The ASUS] sensor emits a 3D point cloud that, along with the robot’s odometry sensor and the incorporated gyroscope, enables Q.bo to build maps, 3D modeling of objects and autonomous localization in real-time. This system can be seen as a more accurate and sophisticated visual perception compared to the stereoscopic cameras or the ultrasound sensors. However, the joint use of all systems (ultrasounds, webcams and Xtion) can generate a much more complete information than the separate use of each.
This 5-minute video shows the Xtion Pro Live sensor being mounted over a prototype mold designed by Thecorpora’s team and then it shows three experiments using Q.bo and the Xtion Pro live sensor:
Real-time 3D visualization of the point cloud emitted by the Xtion Pro Live using the ROS visualization tool called RViz to view Q.bo’s 3D model in a desktop with a NVIDIA GeForce GTX 295 as GPU.
SLAM (Simultaneous Localization And Mapping) in which the robot builds a 2D map of his environment using the laser scan emitted by the Xtion Pro Live sensor. Here the ROS package, Gmapping, is used for the SLAM algorithm developed by Giorgio Grisetti, Cyrill Stachniss and Wolfram Burgard.
Autonomous navigation, reusing the built 2D map stored after using SLAM. The initial location and goal position of Q.bo robot is indicated using the RViz visualization tool. For the autonomous localization, we have used the “amcl” ROS package developed by Brian P. Gerkey. It contains an implementation of a particle filter-based localization algorithm that exploits the laser scan obtained by the Xtion Pro live and the 2D Map. For the movement instructions, we used the ROS package “move_base” developed by Eitan Marder-Eppstein. The “move_base” package contains the implementation of a global and a local 2D motion planners which use the laser scan (emitted by Xtion Pro Live) to detect close obstacles.
If you know a little about quantum mechanics, or computer science, then the idea that there is anything about it that can be tested using the familiar zip compression algorithm will seem as strange as [ ... ]