def test_readings_for_obstacles_regression_1():
"""
Regression test for bug before normalize_radians().
"""
a = 0.299541
b = 0.0500507
laser_angles = lasers.default_laser_angles()
laser_max_range = lasers.default_laser_max_range()
obstacles = np.array([
[-4.475, 1.45, 0.35],
[-1.3, 1.025, 0.35],
[-3., -1.55, 0.35],
[0.65, -1.95, 0.35],
[-1.95, -3.8, 0.35],
[0.15, -5.625, 0.35]])
x = 0.18361848856646254
y = -4.2881577071112806
theta = -2.2011317013010205
lx, ly, ltheta = lasers.car_loc_to_laser_loc(x, y, theta, a, b)
obs_lasers = fast.readings_for_obstacles(
lx, ly, ltheta, laser_angles, laser_max_range, obstacles)
# For debugging when the test fails:
if False:
for i, val in enumerate(obs_lasers):
print i, val
lasers.plot_lasers(
lx, ly, ltheta, laser_angles, laser_max_range,
obstacles, obs_lasers, plt.gca())
plt.show()
nose.tools.assert_almost_equals(obs_lasers[16], 1.89867472652, 10)
nose.tools.assert_almost_equals(obs_lasers[85], 10.0, 10)
nose.tools.assert_almost_equals(obs_lasers[278], 0.739595449593, 10)
def visual_test():
readings_old = readings_for_obstacles_old()
plt.figure('old')
lasers.plot_lasers(
true_x, true_y, true_theta,
laser_angles, laser_max_range,
obstacles, readings_old, plt.gca())
readings_new = readings_for_obstacles_new()
plt.figure('new')
lasers.plot_lasers(
true_x, true_y, true_theta,
laser_angles, laser_max_range,
obstacles, readings_new, plt.gca())
plt.show()
def test_readings_for_obstacles_regression_2():
"""
Regression test for assertion failure in fast_lasers.c.
"""
lx, ly, ltheta = -5.8043534, -2.562654, -0.000112593
laser_angles = lasers.default_laser_angles()
laser_max_range = lasers.default_laser_max_range()
obstacles = np.array([
[4.6, -3.4, 0.68],
])
obs_lasers = fast.readings_for_obstacles(
lx, ly, ltheta, laser_angles, laser_max_range, obstacles)
# For debugging when the test fails:
if False:
for i, val in enumerate(obs_lasers):
print i, val
lasers.plot_lasers(
lx, ly, ltheta, laser_angles, laser_max_range,
obstacles, obs_lasers, plt.gca())
plt.show()
nose.tools.assert_almost_equals(obs_lasers[170], 9.761751166778446, 10)
# LW MAP, state noise 0.01, laser noise 0.01, 100K samples, trial 2:
x = -6.111312885927913
y = -0.11534201599817488
theta = 0.01650033592056109
# Compute laser location from vehicle (rear-axle) location:
a = 0.299541
b = 0.0500507
laser_x, laser_y, laser_theta = car_loc_to_laser_loc(x, y, theta, a, b)
laser_angles = np.arange(-90, 90.5, 0.5) * np.pi / 180
laser_max_range = 10
obstacles = np.array([
[-4.475, 1.45, 0.4],
[-1.3, 1.025, 0.4],
[-3.0, -1.55, 0.4],
[0.65, -1.95, 0.4],
[-1.95, -3.8, 0.4],
[0.15, -5.625, 0.4]
])
plt.figure(figsize=(8, 8))
plot_lasers(
laser_x, laser_y, laser_theta, laser_angles, laser_max_range,
obstacles, lasers, plt.gca())
plt.plot([-7, -7, 7, 7, -7], [-7, 7, 7, -7, -7], 'k')
plt.xlim(-15, 15)
plt.ylim(-15, 15)
plt.show()
