Tricycle calibration using least-squares approach

This repo contains a calibration engine for a front-traction tricycle equipped with encoders and a sensor.

Calibrated parameters

Parameters must be calibrated are the kinematic parameters of the robot and sensor pose (position and orientation) with respect to kinematic center of the robot. In detail:

• k_steer: how many radians correspond to one tick;
• k_traction: how many meters correspond to one tick;
• steer_offset: at which angle corresponds the zero of the wheel;
• base_line: the lenght of the base_line;
• sensor_pose_rel: sensor pose $$(x, y, θ)$$ relative to the robot.

Implementation

All information about implementation and mathematical formalism of the calibration engine are published at the following GitHub Page

Structure overview

In dataset folder there is the text file containing the dataset used to calibrated the robot.

Folder include contains all the header files.

Folder src contains all the cpp files.

In trajectories folder uncalibrated and calibrated trajectories are saved (it already contains some trajectories). When the program is executed, files are overwritten.

File view_dataset_traj.py shows the model pose and the tracker pose in the dataset.

File view_robot_uncalibrated_traj.py shows the model pose (in dataset) and the uncalibrated robot pose.

File view_sensor_uncalibrated_traj.py shows the tracker pose (in dataset) and the uncalibrated sensor pose.

File view_sensor_calibrated_traj.py shows the tracker pose (in dataset) and the calibrated sensor pose.

Installation

Prerequities

To execute the calibration engine:

• Eigen

To visualize uncalibrated and calibrated trajectories using Python scripts:

• numpy
• matplotlib

Build

To build the code, move in the project directory and use the MakeFile:

cd /path/to/robot-calibration-project
make

Results

The Gauss-Newton method is run 7 times. In the first cycle, the entire dataset is used for the calibration. Then, a sumbsampling of the dataset is actuated, putting a threshold for the error norm equals to the mean of the total error accumulated in the previous cycle.
In the end, the calibrated parameters are:

• k_steer: 0.556352;
• k_traction: 0.00947317;
• steer_offset: -0.0506322;
• base_line: 1.35205;
• sensor_pose_rel: (1.58809, 0.0039371, 0.0163697).

The calibrated, uncalibrated and ground truth trajectories are shown below:

Dataset model pose - Dataset tracker pose	Dataset model pose - Uncalibrated robot pose
Dataset tracker pose - Uncalibrated sensor pose	Dataset tracker pose - Calibrated sensor pose