particle filter in python

Here is an implementation of particle filter in Python using numpy:


main.py
import numpy as np

def particle_filter(num_particles, initial_pose, z, motion_model, sensor_model, map):
    # Initialize particles
    particles = np.zeros((num_particles, 3))
    particles[:, :2] = initial_pose[:2] + np.random.normal(0, 1, (num_particles, 2)) * 0.1
    particles[:, 2] = initial_pose[2] + np.random.normal(0, 1, num_particles) * 0.1

    # Iterate over measurements
    for i in range(len(z)):
        # Predict particle motion
        particles = motion_model(particles)

        # Compute particle weights
        weights = sensor_model(particles, z[i], map)

        # Normalize weights
        weights /= np.sum(weights)

        # Resample particles
        indices = np.random.choice(num_particles, num_particles, p=weights)
        particles = particles[indices]

    # Compute estimated position as mean of particles
    estimated_pose = np.mean(particles, axis=0)

    return estimated_pose
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In this implementation, num_particles is the number of particles, initial_pose is the initial pose of the robot (x, y, theta), z is a list of sensor measurements, motion_model is a function that predicts particle motion, sensor_model is a function that computes particle weights based on sensor measurements, and map is a representation of the environment.

The particle_filter function initializes num_particles particles, iterates over the sensor measurements, predicts particle motion using motion_model, computes particle weights using sensor_model, normalizes the weights, resamples particles based on the weights, and finally computes the estimated position as the mean of particles.

Note that motion_model and sensor_model are functions that you need to define based on your specific application.

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