The rc_visard in a nutshell¶

The rc_visard is a self-registering 3D camera. It provides rectified camera, disparity, confidence, and error images, which enable the viewed scene’s depth values along with their uncertainties to be computed. Furthermore, the motion of visual features in the images is combined with acceleration and turn-rate measurements at a high rate, which enables the sensor to provide real-time estimates of its current pose, velocity, and acceleration.

Stereo vision¶

The rc_visard is based on stereo vision using the SGM (Semi-Global Matching) method. In stereo vision, 3D information about a scene can be extracted by comparing two images taken from different viewpoints. The main idea behind using a camera pair for measuring depth is the fact that object points appear at different positions in the two camera images depending on their distance from the camera pair. Very distant object points appear at approximately the same position in both images, whereas very close object points occupy different positions in the left and right camera image. The object points’ displacement in the two images is called disparity. The larger the disparity, the closer the object is to the camera. The principle is illustrated in Fig. 16.

Fig. 16 Sketch of the stereo-vision principle: The more distant object (black) exhibits a smaller disparity $$d_2$$ than that of the close object (gray), $$d_1$$.

Stereo vision is a form of passive sensing, meaning that it emits neither light nor other signals to measure distances, but uses only light that the environment emits or reflects. The rc_visard can thus work indoors and outdoors and multiple rc_visard devices can work together without interferences.

To compute the 3D information, the stereo matching algorithm must be able to find corresponding object points in the left and right camera images. For this, the algorithm requires texture, meaning changes in image intensity values due to patterns or the objects’ surface structure, in the images. Stereo matching is not possible for completely untextured regions, such as a flat white wall without any visible surface structure. The SGM stereo matching method used provides the best trade-off between runtime and accuracy, even for fine structures.

For stereo matching, the position and orientation of the left and right cameras relative to each other has to be known with very high accuracy. This is achieved by calibration. The rc_visard’s cameras are pre-calibrated during production. However, if the rc_visard has been decalibrated, during transport for example, then the user has to recalibrate the stereo camera.

The following rc_visard software components are required to compute 3D information:

• Stereo camera: This component is responsible for capturing synchronized stereo image pairs and transforming them into images approaching those taken by an ideal stereo camera (rectification).
• Stereo matching: This component computes disparities for the rectified stereo camera pair using SGM.
• Camera calibration: This component enables the user to recalibrate the rc_visard’s stereo camera.

Sensor dynamics¶

In addition to providing 3D information about the scene, the rc_visard can also estimate its egomotion or dynamic state in real time. This comprises its current pose, i.e., its position and orientation relative to a reference coordinate system or reference frame, as well as its velocity and acceleration. Measurements from stereo visual odometry (SVO) and the integrated Inertial Measurement Unit (IMU) are fused to compute this information. This combination is called a Visual Inertial Navigation System (VINS).

Visual odometry observes the motion of characteristic points in the camera images to estimate the camera motion. Object points are projected on different pixels in the camera image depending on the camera’s viewing position. Each point’s 3D coordinates can also be computed using stereo matching between the point positions in the left and right camera images. Thus, for two different viewing positions A and B, two sets of corresponding 3D points are computed. Assuming a static environment, the motion that transforms one set of points into the other is the camera’s motion. The principle is illustrated for a simplified 2D case in Fig. 17.

Fig. 17 Simplified sketch of the stereo visual odometry principle for 2D motions: Camera motion is computed from the observed motion of characteristic image points.

Since visual odometry relies on image-data quality, motion estimates deteriorate when the images are blurred or are poorly illuminated. Furthermore, visual odometry’s frame rate is too low for control applications. That’s why the rc_visard has an integrated Inertial Measurement Unit (IMU), which measures the accelerations and angular velocities that occur when the rc_visard moves. It also measures acceleration due to gravity, which 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 individual components.
• Visual odometry: This component computes a motion estimate from the camera images.
• 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.
• SLAM (optional): This component is optionally available for the rc_visard and creates an internal map of the environment, which is used to correct pose errors.

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. The calibration routine can be executed either programmatically via the REST-API interface or manually via the Web GUI.