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. 18.


Fig. 18 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: This component is optionally available for the rc_visard and creates an internal map of the environment, which is used to correct pose errors.