The data is recorded at night, with few street lights illuminating the scene. Our setup was mounted on top of a car which was driving through the streets of a city, collecting time-synchronized data at 2.2 FPS. It demonstrates a challenging condition due to significantly lower illuminance or lux. This require the cameras to capture images at enhanced exposures, while balancing the blur it introduces due to motion. The depth-maps reveal that our stereo-matching algorithm can identify features even in seemingly dark surfaces like the road, tree trunks, and curb.
The full data can be accessed here: AWS S3 link
| Horizontal Field of View | 30 degrees |
|---|---|
| Baseline | 1.17 meters |
| Resolution | 5.4 MP |
| Bit depth | 16 bit |
| Frame rate | 2.2 FPS |
These are vertically concatenated raw images from the left and right camera as shown below:
Assuming pin-hole model for the camera, we get the following intrinsics for our left camera (1) and right camera (2):
| i1_fx = 5276.69 i1_fy = 5276.61 i1_cx = 1453.21 i1_cy = 1005.95 i1_k1 = -0.200407 i1_k2 = 0.399375 i1_k3 = -0.975446 i1_k4 = 0 i1_k5 = 0 i1_k6 = 0 i1_p1 = 0.00343018 i1_p2 = -0.000560687 | i2_fx = 5283.89 i2_fy = 5282.77 i2_cx = 1398.11 i2_cy = 966.162 i2_k1 = -0.207581 i2_k2 = 0.534651 i2_k3 = -1.7257 i2_k4 = 0 i2_k5 = 0 i2_k6 = 0 i2_p1 = 0.00278165 i2_p2 = -0.000319389 |
| --- | --- |
We choose the center of the left rectified image as our frame of reference, where the z-axis faces in the forward direction and the y-axis points in the downward direction. Consequently, the right camera is located along the x-axis in our chosen frame of reference.
The translation (in m) and rotation (in degrees) for the right-camera w.r.t our frame of reference is shown below:
Tx = 1.1699 Ty = 0.0002 Tz = -0.0002 theta_x = 0.3436844382503575 theta_y = -0.07656089029470413 theta_z = 0.157368876500715
We follow the Z-Y-X Euler angle rotation convention. Consequently the overall rotation matrix can be established by: R = R_z * R_y * R_x.
The left-rectified image shows the image from the left camera after rectification, as shown below:
