In the following code I have implemented a localization algorithm based on particle filter.
I have used conda to run my code, you can run the following for installation of dependencies:
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conda create -n Filters python=3 conda activate Filters conda install -c menpo opencv3 conda install numpy scipy matplotlib sympy |
and the code:
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import numpy as np import scipy as scipy from numpy.random import uniform import scipy.stats np.set_printoptions(threshold=3) np.set_printoptions(suppress=True) import cv2 def drawLines(img, points, r, g, b): cv2.polylines(img, [np.int32(points)], isClosed=False, color=(r, g, b)) def drawCross(img, center, r, g, b): d = 5 t = 2 LINE_AA = cv2.LINE_AA if cv2.__version__[0] == '3' else cv2.CV_AA color = (r, g, b) ctrx = center[0,0] ctry = center[0,1] cv2.line(img, (ctrx - d, ctry - d), (ctrx + d, ctry + d), color, t, LINE_AA) cv2.line(img, (ctrx + d, ctry - d), (ctrx - d, ctry + d), color, t, LINE_AA) def mouseCallback(event, x, y, flags,null): global center global trajectory global previous_x global previous_y global zs center=np.array([[x,y]]) trajectory=np.vstack((trajectory,np.array([x,y]))) #noise=sensorSigma * np.random.randn(1,2) + sensorMu if previous_x >0: heading=np.arctan2(np.array([y-previous_y]), np.array([previous_x-x ])) if heading>0: heading=-(heading-np.pi) else: heading=-(np.pi+heading) distance=np.linalg.norm(np.array([[previous_x,previous_y]])-np.array([[x,y]]) ,axis=1) std=np.array([2,4]) u=np.array([heading,distance]) predict(particles, u, std, dt=1.) zs = (np.linalg.norm(landmarks - center, axis=1) + (np.random.randn(NL) * sensor_std_err)) update(particles, weights, z=zs, R=50, landmarks=landmarks) indexes = systematic_resample(weights) resample_from_index(particles, weights, indexes) previous_x=x previous_y=y WIDTH=800 HEIGHT=600 WINDOW_NAME="Particle Filter" #sensorMu=0 #sensorSigma=3 sensor_std_err=5 def create_uniform_particles(x_range, y_range, N): particles = np.empty((N, 2)) particles[:, 0] = uniform(x_range[0], x_range[1], size=N) particles[:, 1] = uniform(y_range[0], y_range[1], size=N) return particles def predict(particles, u, std, dt=1.): N = len(particles) dist = (u[1] * dt) + (np.random.randn(N) * std[1]) particles[:, 0] += np.cos(u[0]) * dist particles[:, 1] += np.sin(u[0]) * dist def update(particles, weights, z, R, landmarks): weights.fill(1.) for i, landmark in enumerate(landmarks): distance=np.power((particles[:,0] - landmark[0])**2 +(particles[:,1] - landmark[1])**2,0.5) weights *= scipy.stats.norm(distance, R).pdf(z[i]) weights += 1.e-300 # avoid round-off to zero weights /= sum(weights) def neff(weights): return 1. / np.sum(np.square(weights)) def systematic_resample(weights): N = len(weights) positions = (np.arange(N) + np.random.random()) / N indexes = np.zeros(N, 'i') cumulative_sum = np.cumsum(weights) i, j = 0, 0 while i < N and j<N: if positions[i] < cumulative_sum[j]: indexes[i] = j i += 1 else: j += 1 return indexes def estimate(particles, weights): pos = particles[:, 0:1] mean = np.average(pos, weights=weights, axis=0) var = np.average((pos - mean)**2, weights=weights, axis=0) return mean, var def resample_from_index(particles, weights, indexes): particles[:] = particles[indexes] weights[:] = weights[indexes] weights /= np.sum(weights) x_range=np.array([0,800]) y_range=np.array([0,600]) #Number of partciles N=400 landmarks=np.array([ [144,73], [410,13], [336,175], [718,159], [178,484], [665,464] ]) NL = len(landmarks) particles=create_uniform_particles(x_range, y_range, N) weights = np.array([1.0]*N) # Create a black image, a window and bind the function to window img = np.zeros((HEIGHT,WIDTH,3), np.uint8) cv2.namedWindow(WINDOW_NAME) cv2.setMouseCallback(WINDOW_NAME,mouseCallback) center=np.array([[-10,-10]]) trajectory=np.zeros(shape=(0,2)) robot_pos=np.zeros(shape=(0,2)) previous_x=-1 previous_y=-1 DELAY_MSEC=50 while(1): cv2.imshow(WINDOW_NAME,img) img = np.zeros((HEIGHT,WIDTH,3), np.uint8) drawLines(img, trajectory, 0, 255, 0) drawCross(img, center, r=255, g=0, b=0) #landmarks for landmark in landmarks: cv2.circle(img,tuple(landmark),10,(255,0,0),-1) #draw_particles: for particle in particles: cv2.circle(img,tuple((int(particle[0]),int(particle[1]))),1,(255,255,255),-1) if cv2.waitKey(DELAY_MSEC) & 0xFF == 27: break cv2.circle(img,(10,10),10,(255,0,0),-1) cv2.circle(img,(10,30),3,(255,255,255),-1) cv2.putText(img,"Landmarks",(30,20),1,1.0,(255,0,0)) cv2.putText(img,"Particles",(30,40),1,1.0,(255,255,255)) cv2.putText(img,"Robot Trajectory(Ground truth)",(30,60),1,1.0,(0,255,0)) drawLines(img, np.array([[10,55],[25,55]]), 0, 255, 0) cv2.destroyAllWindows() |
Hi,
I am a complete BEGINNER in robot navigation. please bare with my questions and please do answer them. It would really help me alot for my project.
I have an ultrasonic sensor, GPS module and raspberry pi. I also have the locations of different way points the robot is supposed to go to. Can you tell me what the sensor limitations would be if i use this code for localization? and how should i execute the code, i mean just copy paste it and run in raspberry pi and the robot will start localizing itself?
Thanks
great information,thanks
Nice example,
is this code release with any license?
Thanks
Hi, you can use it for what ever you want, but that would be nice to give some credit to my website or GitHub repository.
This helped a lot. Thank you!
Hi, thank you for the post, it has really helped me understand the concept. One thing which I am facing difficulty to understand is systematic_resampling function, can you please elaborate on that?
https://nbviewer.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb
This might be help to understand systematic resampling.
how to run this code can you explain