Technical Specs

gives global orientation in the vertical direction. Further, IMU measurements have a high rate of 200 Hz. The
rc_visard’s linear velocity, position, and orientation can be computed by integrating the IMU measurements.
However, the integration results suffer from increasing drift over time. The rc_visard thus fuses accurate, but
low-frequency and sometimes volatile visual odometry measurements with reliable high-rate IMU measurements
to provide an accurate, robust, high-frequency estimate of the rc_visard’s current position, orientation, velocity,
and acceleration, which can be used in a control loop.
In addition to the stereo camera component and the calibration component, pose-estimate computations require
the following rc_visard software components:
• Sensor dynamics: This component handles starting, stopping, and streaming of the estimates for the indi-
vidual components (Section 6.3).
– Visual odometry: This component computes a motion estimate from the camera images (Section 6.4).
– Stereo INS: This component fuses the motion estimates from visual odometry with the measurements
from the integrated IMU to provide real-time pose estimates at a high frequency (Section 6.5).
– SLAM: This component is optionally available for the rc_visard and creates an internal map of the
environment, which is used to correct pose errors (Section 7.1).
5.3 Calibration relative to a robot
The rc_visard is designed for industrial environments including those featuring robotic applications in which the
rc_visard is either mounted on a robot or statically in a robot work cell. To use the rc_visard’s output, the robot
must know where the sensor is located in the robot coordinate frame. To compute the rc_visard’s location in the
robot coordinate frame, the sensor offers the so-called Hand-eye calibration software component (Section 6.7).
The calibration routine can be executed either programmatically via the REST-API interface or manually via the
Web GUI (Section 4.5).
5.3. Calibration relative to a robot 26