Calibration

Occasionally, we discover that the LiDAR extrinsic parameters are inaccurate. In such cases, we aim to recalibrate the SLAM poses and maps based on the updated parameters, without the need to rerun the entire SLAM process. By doing so, we can keep the annotations and the original SLAM map, which saves human effort and computational resources. Raw Data Given LiDAR points in the LiDAR coordinate system $$\mathbf{p}^{orig} = \bigcup_{t=0:T}\{\mathbf{p}_{t,i}\}_{i=1}^{N_t}$$Where $t$ is the time index, $i$ is the point index at time $t$, and $N_t$ is the number of points at time $t$. ...

In this post, we will discuss the perspective-n-point (PnP) problem. We will start with the problem definition. Then, gradient-based optimization methods will be introduced. Finally, we will discuss two global optimization methods. Problem Formulation The core task of the Perspective-n-Point (PnP) problem is to determine the pose—specifically, the rotation and translation—of a calibrated camera in 3D space. This is achieved by using a set of known 3D points in the world and their corresponding 2D projections observed on the camera’s image sensor. ...

This post is an application of the EOL calibration described in the EOL calibration article. Detect Image Corners Detecting the corners of the fisheye images on its original image is challenging due to its severe distortion. So we resort to detecting the corners on the BEV image and then project the corners back onto the original image to further refine the corners. Set initial extrinsics (referring to installation parameters (angles and positions) or parameters from joint calibration with LiDAR), construct a 20m×20m grid with resolution of 0.01m in the ego coordinate system’s ground plane (z=0), and generate a BEV projected image; ...