def __init__(self, params):
# Set the variables:
# Minimize the reprojection error through Bundle Adjustment
# Set minimum number of measured points
# Set maximum number of loop interations for map correction
self.optimizer = BundleAdjustment()
self.min_measurements = params.pnp_min_measurements
self.max_iterations = params.pnp_max_iterations
def refine_pose(self, pose, cam, measurements):
assert len(measurements) >= self.min_measurements, (
'Not enough points')
self.optimizer = BundleAdjustment()
self.optimizer.add_pose(0, pose, cam, fixed=False)
for i, m in enumerate(measurements):
self.optimizer.add_point(i, m.mappoint.position, fixed=True)
self.optimizer.add_edge(0, i, 0, m)
self.optimizer.optimize(self.max_iterations)
return self.optimizer.get_pose(0)
def __init__(self, params, cam):
self.params = params
self.cam = cam
self.motion_model = MotionModel()
self.map = Map()
self.preceding = None # last keyframe
self.current = None # current frame
self.status = defaultdict(bool)
self.optimizer = BundleAdjustment()
self.bundle_adjustment = LocalBA()
self.min_measurements = params.pnp_min_measurements
self.max_iterations = params.pnp_max_iterations
self.timer = RunningAverageTimer()
self.lines = True
def __init__(self, params):
self.optimizer = BundleAdjustment()
self.min_measurements = params.pnp_min_measurements
self.max_iterations = params.pnp_max_iterations
class Tracking(object):
def __init__(self, params):
self.optimizer = BundleAdjustment()
self.min_measurements = params.pnp_min_measurements
self.max_iterations = params.pnp_max_iterations
def refine_pose(self, pose, cam, measurements):
assert len(measurements) >= self.min_measurements, (
'Not enough points')
self.optimizer.clear()
self.optimizer.add_pose(0, pose, cam, fixed=False)
for i, m in enumerate(measurements):
self.optimizer.add_point(i, m.mappoint.position, fixed=True)
self.optimizer.add_edge(0, i, 0, m)
self.optimizer.optimize(self.max_iterations)
return self.optimizer.get_pose(0)
class Tracker(object):
def __init__(self, params, cam):
self.params = params
self.cam = cam
self.motion_model = MotionModel()
self.map = Map()
self.preceding = None # last keyframe
self.current = None # current frame
self.status = defaultdict(bool)
self.optimizer = BundleAdjustment()
self.bundle_adjustment = LocalBA()
self.min_measurements = params.pnp_min_measurements
self.max_iterations = params.pnp_max_iterations
self.timer = RunningAverageTimer()
self.lines = True
def initialize(self, frame):
keyframe = frame.to_keyframe()
mappoints, measurements = keyframe.create_mappoints_from_triangulation(
)
assert len(mappoints) >= self.params.init_min_points, (
'Not enough points to initialize map.')
keyframe.set_fixed(True)
self.extend_graph(keyframe, mappoints, measurements)
if self.lines:
maplines, line_measurements = keyframe.create_maplines_from_triangulation(
)
print(f'Initialized {len(maplines)} lines')
for mapline, measurement in zip(maplines, line_measurements):
self.map.add_mapline(mapline)
self.map.add_line_measurement(keyframe, mapline, measurement)
keyframe.add_measurement(measurement)
mapline.add_measurement(measurement)
self.preceding = keyframe
self.current = keyframe
self.status['initialized'] = True
self.motion_model.update_pose(frame.timestamp, frame.position,
frame.orientation)
# def clear_optimizer(self):
# # Calling optimizer.clear() doesn't fully clear for some reason
# # This prevents running time from scaling linearly with the number of frames
# self.optimizer = BundleAdjustment()
# self.bundle_adjustment = LocalBA()
def refine_pose(self, pose, cam, measurements):
assert len(measurements) >= self.min_measurements, (
'Not enough points')
self.optimizer = BundleAdjustment()
self.optimizer.add_pose(0, pose, cam, fixed=False)
for i, m in enumerate(measurements):
self.optimizer.add_point(i, m.mappoint.position, fixed=True)
self.optimizer.add_edge(0, i, 0, m)
self.optimizer.optimize(self.max_iterations)
return self.optimizer.get_pose(0)
def update(self, i, left_img, right_img, timestamp):
# Feature extraction takes 0.12s
origin = g2o.Isometry3d()
left_frame = Frame(i, origin, self.cam, self.params, left_img,
timestamp)
right_frame = Frame(i, self.cam.compute_right_camera_pose(origin),
self.cam, self.params, right_img, timestamp)
frame = StereoFrame(left_frame, right_frame)
if i == 0:
self.initialize(frame)
return
# All code in this functions below takes 0.05s
self.current = frame
predicted_pose, _ = self.motion_model.predict_pose(frame.timestamp)
frame.update_pose(predicted_pose)
# Get mappoints and measurements take 0.013s
local_mappoints = self.get_local_map_points(frame)
print(local_mappoints)
if len(local_mappoints) == 0:
print('Nothing in local_mappoints! Exiting.')
exit()
measurements = frame.match_mappoints(local_mappoints)
# local_maplines = self.get_local_map_lines(frame)
# line_measurements = frame.match_maplines(local_maplines)
# Refined pose takes 0.02s
try:
pose = self.refine_pose(frame.pose, self.cam, measurements)
frame.update_pose(pose)
self.motion_model.update_pose(frame.timestamp, pose.position(),
pose.orientation())
tracking_is_ok = True
except:
tracking_is_ok = False
print('tracking failed!!!')
if tracking_is_ok and self.should_be_keyframe(frame, measurements):
# Keyframe creation takes 0.03s
self.create_new_keyframe(frame)
# self.optimize_map()
def optimize_map(self):
"""
Python doesn't really work with the multithreading model, so just putting optimization on the main thread
"""
adjust_keyframes = self.map.search_adjust_keyframes()
# Set data time increases with iterations!
# self.timer = RunningAverageTimer()
self.bundle_adjustment = LocalBA()
self.bundle_adjustment.optimizer.set_verbose(True)
# with self.timer:
self.bundle_adjustment.set_data(adjust_keyframes, [])
self.bundle_adjustment.optimize(2)
self.bundle_adjustment.update_poses()
self.bundle_adjustment.update_points()
def extend_graph(self, keyframe, mappoints, measurements):
self.map.add_keyframe(keyframe)
for mappoint, measurement in zip(mappoints, measurements):
self.map.add_mappoint(mappoint)
self.map.add_point_measurement(keyframe, mappoint, measurement)
keyframe.add_measurement(measurement)
mappoint.add_measurement(measurement)
def create_new_keyframe(self, frame):
keyframe = frame.to_keyframe()
keyframe.update_preceding(self.preceding)
mappoints, measurements = keyframe.create_mappoints_from_triangulation(
)
self.extend_graph(keyframe, mappoints, measurements)
if self.lines:
maplines, line_measurements = keyframe.create_maplines_from_triangulation(
)
frame.visualise_measurements(line_measurements)
print(f'New Keyframe with {len(maplines)} lines')
for mapline, measurement in zip(maplines, line_measurements):
self.map.add_mapline(mapline)
self.map.add_line_measurement(keyframe, mapline, measurement)
keyframe.add_measurement(measurement)
mapline.add_measurement(measurement)
self.preceding = keyframe
def get_local_map_points(self, frame):
checked = set()
filtered = []
# Add in map points from preceding and reference
for pt in self.preceding.mappoints(): # neglect can_view test
# if pt in checked or pt.is_bad():
# print('bad')
# continue
pt.increase_projection_count()
filtered.append(pt)
return filtered
def get_local_map_lines(self, frame):
checked = set()
filtered = []
# Add in map points from preceding and reference
for ln in self.preceding.maplines(): # neglect can_view test
if ln in checked or ln.is_bad():
continue
ln.increase_projection_count()
filtered.append(ln)
return filtered
def should_be_keyframe(self, frame, measurements):
n_matches = len(measurements)
n_matches_ref = len(self.preceding.measurements())
return ((n_matches / n_matches_ref) <
self.params.min_tracked_points_ratio) or n_matches < 20
class Tracking(object):
def __init__(self, params):
# Set the variables:
# Minimize the reprojection error through Bundle Adjustment
# Set minimum number of measured points
# Set maximum number of loop interations for map correction
self.optimizer = BundleAdjustment()
self.min_measurements = params.pnp_min_measurements
self.max_iterations = params.pnp_max_iterations
# STEP - REFINE POSE
def refine_pose(self, pose, cam, measurements):
# Check if the minimum number of measured points is reached
assert len(measurements) >= self.min_measurements, (
'Not enough points')
# Clear Bundle Adjustment (reprojection error) and add the currently measured position of the robot
self.optimizer.clear()
self.optimizer.add_pose(0, pose, cam, fixed=False)
for i, m in enumerate(measurements):
self.optimizer.add_point(i, m.mappoint.position, fixed=True)
self.optimizer.add_edge(0, i, 0, m)
self.optimizer.optimize(self.max_iterations)
return self.optimizer.get_pose(0)
