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Evaluating subtrajectories with ATE #34

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fe8e0a0
Evaluate subtrajectories with ATE
tcies Nov 1, 2020
64a46e5
RMSE, also plot error history
tcies Nov 2, 2020
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2 changes: 2 additions & 0 deletions .gitignore
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@@ -0,0 +1,2 @@
*.pyc

4 changes: 1 addition & 3 deletions scripts/analyze_trajectories.py
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Expand Up @@ -440,11 +440,9 @@ def parse_config_file(config_fn, sort_names):
os.makedirs(cur_res_dir)
datasets_res_dir[d] = cur_res_dir
same_subtraj = True if rel_e_distances else False
assert len(PALLETE) > len(algorithms),\
"Not enough colors for all configurations"
algo_colors = {}
for i in range(len(algorithms)):
algo_colors[algorithms[i]] = PALLETE[i]
algo_colors[algorithms[i]] = PALLETE[i % len(PALLETE)]

print(Fore.YELLOW+"=== Evaluation Configuration Summary ===")
print(Fore.YELLOW+"Datasests to evaluate: ")
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120 changes: 77 additions & 43 deletions src/rpg_trajectory_evaluation/compute_trajectory_errors.py
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Expand Up @@ -5,13 +5,14 @@
import os
import numpy as np

import align_utils
import trajectory_utils as tu
import transformations as tf


def compute_relative_error(p_es, q_es, p_gt, q_gt, T_cm, dist, max_dist_diff,
accum_distances=[],
scale=1.0):
scale=1.0, method='pos_yaw_align', debug=False):

if len(accum_distances) == 0:
accum_distances = tu.get_distance_from_start(p_gt)
Expand All @@ -30,48 +31,82 @@ def compute_relative_error(p_es, q_es, p_gt, q_gt, T_cm, dist, max_dist_diff,
# IPython.embed()
for idx, c in enumerate(comparisons):
if not c == -1:
T_c1 = tu.get_rigid_body_trafo(q_es[idx, :], p_es[idx, :])
T_c2 = tu.get_rigid_body_trafo(q_es[c, :], p_es[c, :])
T_c1_c2 = np.dot(np.linalg.inv(T_c1), T_c2)
T_c1_c2[:3, 3] *= scale

T_m1 = tu.get_rigid_body_trafo(q_gt[idx, :], p_gt[idx, :])
T_m2 = tu.get_rigid_body_trafo(q_gt[c, :], p_gt[c, :])
T_m1_m2 = np.dot(np.linalg.inv(T_m1), T_m2)

T_m1_m2_in_c1 = np.dot(T_cm, np.dot(T_m1_m2, T_mc))
T_error_in_c2 = np.dot(np.linalg.inv(T_m1_m2_in_c1), T_c1_c2)
T_c2_rot = np.eye(4)
T_c2_rot[0:3, 0:3] = T_c2[0:3, 0:3]
T_error_in_w = np.dot(T_c2_rot, np.dot(
T_error_in_c2, np.linalg.inv(T_c2_rot)))
errors.append(T_error_in_w)
p_gt_i = p_gt[idx:c, :]
q_gt_i = q_gt[idx:c, :]
p_es_i = p_es[idx:c, :]
q_es_i = q_es[idx:c, :]

plt.figure(0, figsize=(10, 5))
plt.clf()
ax1 = plt.subplot(1, 2, 1)
ax1.plot(p_es[:, 0], p_es[:, 1])
ax1.plot(p_gt[:, 0], p_gt[:, 1])
ax1.plot(p_es[idx, 0], p_es[idx, 1], 'rx')
ax1.plot(p_es[c, 0], p_es[c, 1], 'rx')
ax1.plot(p_gt[idx, 0], p_gt[idx, 1], 'rx')
ax1.plot(p_gt[c, 0], p_gt[c, 1], 'rx')
ax1.set_aspect('equal')


ax2 = plt.subplot(1, 2, 2)
gt_portion = p_gt[idx:c, :]
gt_al0 = np.dot(np.linalg.inv(T_m1)[:3, :3], gt_portion.T) + np.linalg.inv(T_m1)[:3, 3].reshape([3, 1])
es_portion = p_es[idx:c, :]
es_al0 = np.dot(np.linalg.inv(T_c1)[:3, :3], es_portion.T) + np.linalg.inv(T_c1)[:3, 3].reshape([3, 1])
ax2.plot(es_al0[0, :], es_al0[1, :], label='es')
ax2.plot(gt_al0[0, :], gt_al0[1, :], label='gt')
ax2.axis([-10, 10, -10, 10])
ax2.set_aspect('equal')
plt.legend()
plt.savefig('debug/%04d.png' % idx)

#IPython.embed()
if method == 'initial_pose':
T_c1 = tu.get_rigid_body_trafo(q_es[idx, :], p_es[idx, :])
T_c2 = tu.get_rigid_body_trafo(q_es[c, :], p_es[c, :])
T_c1_c2 = np.dot(np.linalg.inv(T_c1), T_c2)
T_c1_c2[:3, 3] *= scale

T_m1 = tu.get_rigid_body_trafo(q_gt[idx, :], p_gt[idx, :])
T_m2 = tu.get_rigid_body_trafo(q_gt[c, :], p_gt[c, :])
T_m1_m2 = np.dot(np.linalg.inv(T_m1), T_m2)

T_m1_m2_in_c1 = np.dot(T_cm, np.dot(T_m1_m2, T_mc))
T_error_in_c2 = np.dot(np.linalg.inv(T_m1_m2_in_c1), T_c1_c2)
T_c2_rot = np.eye(4)
T_c2_rot[0:3, 0:3] = T_c2[0:3, 0:3]
T_error_in_w = np.dot(T_c2_rot, np.dot(
T_error_in_c2, np.linalg.inv(T_c2_rot)))
errors.append(T_error_in_w)

if debug:
gt_al0 = np.dot(np.linalg.inv(T_m1)[:3, :3], p_gt_i.T) + np.linalg.inv(T_m1)[:3, 3].reshape([3, 1])
es_al0 = np.dot(np.linalg.inv(T_c1)[:3, :3], p_es_i.T) + np.linalg.inv(T_c1)[:3, 3].reshape([3, 1])

else:
assert method == 'pos_yaw_align'
# IPython.embed()
s, R_gt_es, t_gt_es = align_utils.alignTrajectory(p_es_i, p_gt_i, q_es_i, q_gt_i, method='posyaw')
assert s == 1.
p_es_i_aligned = (np.dot(R_gt_es, p_es_i.T) + t_gt_es.reshape((3, 1))).T
T_error_dummy = np.eye(4)
e_trans_vec = p_gt_i - p_es_i_aligned
T_error_dummy[0, 3] = np.sqrt(np.mean(np.sum(e_trans_vec**2, 1)))
errors.append(T_error_dummy)

if debug:
T_m1 = tu.get_rigid_body_trafo(q_gt[idx, :], p_gt[idx, :])
gt_al0 = np.dot(np.linalg.inv(T_m1)[:3, :3], p_gt_i.T) + np.linalg.inv(T_m1)[:3, 3].reshape([3, 1])
es_al0 = np.dot(np.linalg.inv(T_m1)[:3, :3], p_es_i_aligned.T) + np.linalg.inv(T_m1)[:3, 3].reshape([3, 1])



if debug:
plt.figure(0, figsize=(15, 5))
plt.clf()
ax1 = plt.subplot(1, 3, 1)
ax1.plot(p_es[:, 0], p_es[:, 1])
ax1.plot(p_gt[:, 0], p_gt[:, 1])
ax1.plot(p_es[idx, 0], p_es[idx, 1], 'rx')
ax1.plot(p_es[c, 0], p_es[c, 1], 'rx')
ax1.plot(p_gt[idx, 0], p_gt[idx, 1], 'rx')
ax1.plot(p_gt[c, 0], p_gt[c, 1], 'rx')
ax1.set_title('Full traj., sample source')
ax1.set_aspect('equal')


ax2 = plt.subplot(1, 3, 2)
ax2.plot(es_al0[0, :], es_al0[1, :], label='es')
ax2.plot(gt_al0[0, :], gt_al0[1, :], label='gt')
# ax2.axis([-dist, dist, -dist, dist])
ax2.set_aspect('equal')
ax2.set_title('Error is %f m' % np.linalg.norm(errors[-1][:3, 3]))
plt.legend()

ax3 = plt.subplot(1, 3, 3)
x = range(len(comparisons))
y = [np.linalg.norm(e[0:3, 3]) for e in errors] + (len(comparisons) - len(errors)) * [None]
ax3.plot(x, y)
ax3.set_title('All errors')

if not os.path.exists('debug'):
os.makedirs('debug')
plt.savefig('debug/%04d.png' % idx)

error_trans_norm = []
error_trans_perc = []
Expand All @@ -89,7 +124,6 @@ def compute_relative_error(p_es, q_es, p_gt, q_gt, T_cm, dist, max_dist_diff,
error_gravity.append(
np.sqrt(ypr_angles[1]**2+ypr_angles[2]**2)*180.0/np.pi)
e_rot_deg_per_m.append(e_rot[-1] / dist)
IPython.embed()
return errors, np.array(error_trans_norm), np.array(error_trans_perc),\
np.array(error_yaw), np.array(error_gravity), np.array(e_rot),\
np.array(e_rot_deg_per_m)
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3 changes: 0 additions & 3 deletions src/rpg_trajectory_evaluation/trajectory_utils.py
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Expand Up @@ -34,9 +34,6 @@ def compute_comparison_indices_length(distances, dist, max_dist_diff):
error = np.abs(distances[i] - (d+dist))
if best_idx != -1:
comparisons.append(best_idx)
print("%d to %d" % (idx, best_idx))
else:
print("No best match for index %d!" % idx)
return comparisons


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