ninadkale98 / sensor_fusion

Sensor Fusion Algorithms

This repo provides examples of popular filtering algorithms for state estimation. Currently, the following algorithms are availbl:

• Extended Kalman Filter (EKF)
• Unscented Kalman Filter (UKF)
• Particle Filter (PF)

An indepth note on probability theory and the state estimation equations is provided.

📘 Usage

Run the RoverTests.py and RacingTests.py files to test the filters.

Rover Tests

The task of localising a rover vehicle is used to evaluate the filtering algorithms. The vehicle travels at constant speed with a steering angle input. Non-linear dynamics are used to represent the position updates, based on the rover orientation. The rover receives measurement updates from a beacon that provides a distance and angle from the vehcile. The task is to use these two sources of information to estimate the rovers position. Full equations for the rover, and the derivation of the Jacobians are given in the Rover note.

Racing Tests

Since the most common application of the particle filter is robot localisation, an example of autonomous racing is included. The same rover dynamics are used, but a controller that moves the vehicle around a track is introduced. Instead of a beacon, a LiDAR scanner is used and a method is provided to simulate LiDAR scans. The goal of the task is to estimate the vehicle's position as accurately as possible.

Results

Results from the rover localisation tests.

Particle filter, LiDAR based localisation:

Planning improvements

• Add the Linear Kalman Filter (LKF)
• Add more visualisations