Robots Using ROS: Thecorpora's Qbo

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Qbos.jpg

Qbo is a personal, open-source robot being developed by Thecorpora. Francisco Paz started the Qbo project five years ago to address the need for a low cost, open-source robot to enable the ordinary consumer to enter the robotics and the artificial intelligence world.

A couple months ago, Thecorpora decided to switch their software development to ROS and have now acheived "99.9%" integration. You can watch the video below of Qbo's head servos being controlled by the ROS Wiimote drivers, as well as this video of the Wiimote controlling Qbo's wheels. Their use of the ROS joystick drivers means that any of the supported joysticks can be used with Qbo, including the PS3 joystick and generic Linux joysticks.

Qbo's many other sensors are also integrated with ROS, which means that they can be used with higher-level ROS libraries. This includes the four ultrasonic sensors as well as Qbo's stereo webcams. They have already integrated the stereo and odometry data with OpenCV in order to provide SLAM capabilities (described below).

It's really exciting to see an open-source robot building and extending upon ROS. From their latest status update, it sounds like things are getting close to done, including a nice GUI that lets even novice users interact with the robot.

Qbo SLAM algorithm:

The algorithm can be divided into three different parts:

The first task is to calculate the movement of the robot. To do that we use the driver for our robot that sends an Odometry message.

The second task is to detect natural features in the images and estimate their positions in a three dimensional space. The algorithm used to detect the features is the GoodFeaturesToTrackDetector function from OpenCV. Then we extract SURF descriptors of those features and match them with the BruteForceMatcher algorithm, also from OpenCV.

We also track the points matched with the sparse iterative version of the Lucas-Kanade optical flow in pyramids and avoid looking for new features in places where we are already tracking another feature.

We take the images to this node from image messages synchronized and send a PointCloud message with the position of the features, their covariance in the three coordinates, and the SURF descriptor of the features.

The third task is to implement an Extended Kalman Filter and a data association algorithm based in the mahalanobis distance from the CloudPoint seen from the robot and the CloudPoint of the map. To do that we read the Odometry and PointCloud messages and we send also an Odometry message and a PointCloud message with the position of the robot and the features included in the map as an output.

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This page contains a single entry by kwc published on August 23, 2010 11:53 AM.

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