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simulation.py
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simulation.py
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import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
from lib.plotting import (
plot_connections, plot_history, plot_landmarks, plot_measurement,
plot_particles_weight, plot_particles_grey, plot_confidence_ellipse,
plot_sensor_fov, plot_map
)
from lib.particle3 import FlatParticle
import json
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.autoinit import context
from pycuda.driver import limit
from lib.sensor import Sensor, wrap_angle
from lib.stats import Stats
from lib.common import CUDAMemory, resample, rescale, get_pose_estimate
from cuda.fastslam import load_cuda_modules
def run_SLAM(config, plot=False, seed=None):
if seed is None:
seed = config.SEED
np.random.seed(seed)
assert config.THREADS <= 1024 # cannot run more in a single block
assert config.N >= config.THREADS
assert config.N % config.THREADS == 0
particles = FlatParticle.get_initial_particles(config.N, config.MAX_LANDMARKS, config.START_POSITION.copy(), sigma=0.2)
if plot:
fig, ax = plt.subplots(2, 1, figsize=(10, 5))
ax[0].axis('scaled')
ax[1].axis('scaled')
sensor = Sensor(
config.LANDMARKS, [],
config.sensor.VARIANCE, config.sensor.RANGE,
config.sensor.FOV, config.sensor.MISS_PROB, 0, rb=True
)
cuda_modules = load_cuda_modules(
THREADS=config.THREADS,
PARTICLE_SIZE=config.PARTICLE_SIZE,
N_PARTICLES=config.N
)
memory = CUDAMemory(config)
weights = np.zeros(config.N, dtype=np.float64)
cuda.memcpy_htod(memory.cov, 8 * config.sensor.COVARIANCE)
cuda.memcpy_htod(memory.particles, particles)
cuda_modules["predict"].get_function("init_rng")(
np.int32(seed), block=(config.THREADS, 1, 1), grid=(config.N//config.THREADS, 1, 1)
)
stats = Stats("Loop", "Measurement")
stats.add_pose(config.START_POSITION.tolist(), config.START_POSITION.tolist())
pose = config.START_POSITION.copy()
for i in range(config.CONTROL.shape[0]):
stats.start_measuring("Loop")
stats.start_measuring("Measurement")
pose[0] += (config.DT * config.CONTROL[i, 1]) * np.cos(pose[2])
pose[1] += (config.DT * config.CONTROL[i, 1]) * np.sin(pose[2])
pose[2] += (config.DT * config.CONTROL[i, 0])
pose[2] = wrap_angle(pose[2])
measurements = sensor.get_noisy_measurements(pose)
visible_measurements = measurements["observed"]
missed_landmarks = measurements["missed"]
out_of_range_landmarks = measurements["outOfRange"]
stats.stop_measuring("Measurement")
cuda_modules["resample"].get_function("reset_weights")(
memory.particles,
block=(config.THREADS, 1, 1), grid=(config.N//config.THREADS, 1, 1)
)
cuda.memcpy_htod(memory.measurements, visible_measurements)
cuda_modules["predict"].get_function("predict_from_model")(
memory.particles,
np.float64(config.CONTROL[i, 0]), np.float64(config.CONTROL[i, 1]),
np.float64(config.CONTROL_VARIANCE[0] ** 0.5), np.float64(config.CONTROL_VARIANCE[1] ** 0.5),
np.float64(config.DT),
block=(config.THREADS, 1, 1), grid=(config.N//config.THREADS, 1, 1)
)
cuda_modules["update"].get_function("update")(
memory.particles, np.int32(config.N//config.THREADS),
memory.scratchpad, np.int32(memory.scratchpad_block_size),
memory.measurements,
np.int32(config.N), np.int32(len(visible_measurements)),
memory.cov, np.float64(config.THRESHOLD),
np.float64(config.sensor.RANGE), np.float64(config.sensor.FOV),
np.int32(config.MAX_LANDMARKS),
block=(config.THREADS, 1, 1)
)
rescale(cuda_modules, config, memory)
estimate = get_pose_estimate(cuda_modules, config, memory)
stats.add_pose(pose.tolist(), estimate.tolist())
if plot:
cuda.memcpy_dtoh(particles, memory.particles)
visible_measurements = [[r*np.cos(b + pose[2]), r*np.sin(b + pose[2])]
for (r, b) in visible_measurements]
visible_measurements = np.array(visible_measurements)
ax[0].clear()
ax[1].clear()
ax[0].axis('scaled')
ax[1].axis('scaled')
plot_sensor_fov(ax[0], pose, config.sensor.RANGE, config.sensor.FOV)
plot_sensor_fov(ax[1], pose, config.sensor.RANGE, config.sensor.FOV)
if(visible_measurements.size != 0):
plot_connections(ax[0], pose, visible_measurements + pose[:2])
plot_landmarks(ax[0], config.LANDMARKS, color="blue", zorder=100)
plot_landmarks(ax[0], out_of_range_landmarks, color="black", zorder=101)
plot_history(ax[0], stats.ground_truth_path, color='green')
plot_history(ax[0], stats.predicted_path, color='orange')
plot_particles_weight(ax[0], particles)
if(visible_measurements.size != 0):
plot_measurement(ax[0], pose[:2], visible_measurements, color="orange", zorder=103)
plot_landmarks(ax[0], missed_landmarks, color="red", zorder=102)
best = np.argmax(FlatParticle.w(particles))
plot_landmarks(ax[1], config.LANDMARKS, color="black")
covariances = FlatParticle.get_covariances(particles, best)
plot_map(ax[1], FlatParticle.get_landmarks(particles, best), color="orange", marker="o")
for i, landmark in enumerate(FlatParticle.get_landmarks(particles, best)):
plot_confidence_ellipse(ax[1], landmark, covariances[i], n_std=3)
plt.pause(0.001)
cuda_modules["weights_and_mean"].get_function("get_weights")(
memory.particles, memory.weights,
block=(config.THREADS, 1, 1), grid=(config.N//config.THREADS, 1, 1)
)
cuda.memcpy_dtoh(weights, memory.weights)
neff = FlatParticle.neff(weights)
if neff < 0.6*config.N:
resample(cuda_modules, config, weights, memory, 0.5)
stats.stop_measuring("Loop")
stats.summary()
memory.free()
return stats.mean_path_deviation()
if __name__ == "__main__":
from config_simulation import config
context.set_limit(limit.MALLOC_HEAP_SIZE, config.GPU_HEAP_SIZE_BYTES)
run_SLAM(config, plot=True)