def initialize(self, f_cur, img_cur):
if self.num_failures > kNumOfFailuresAfterWichNumMinTriangulatedPointsIsHalved:
self.num_min_triangulated_points = 0.5 * Parameters.kInitializerNumMinTriangulatedPoints
self.num_failures = 0
Printer.orange(
'Initializer: halved min num triangulated features to ',
self.num_min_triangulated_points)
# prepare the output
out = InitializerOutput()
is_ok = False
#print('num frames: ', len(self.frames))
# if too many frames have passed, move the current idx_f_ref forward
# this is just one very simple policy that can be used
if self.f_ref is not None:
if f_cur.id - self.f_ref.id > kMaxIdDistBetweenIntializingFrames:
self.f_ref = self.frames[-1] # take last frame in the buffer
#self.idx_f_ref = len(self.frames)-1 # take last frame in the buffer
#self.idx_f_ref = self.frames.index(self.f_ref) # since we are using a deque, the code of the previous commented line is not valid anymore
#print('*** idx_f_ref:',self.idx_f_ref)
#self.f_ref = self.frames[self.idx_f_ref]
f_ref = self.f_ref
#print('ref fid: ',self.f_ref.id,', curr fid: ', f_cur.id, ', idxs_ref: ', self.idxs_ref)
# append current frame
self.frames.append(f_cur)
# if the current frames do no have enough features exit
if len(f_ref.kps) < self.num_min_features or len(
f_cur.kps) < self.num_min_features:
Printer.red('Inializer: ko - not enough features!')
self.num_failures += 1
return out, is_ok
# find keypoint matches
idxs_cur, idxs_ref = match_frames(f_cur, f_ref,
kFeatureMatchRatioTestInitializer)
#print('|------------')
#print('deque ids: ', [f.id for f in self.frames])
#print('initializing frames ', f_cur.id, ', ', f_ref.id)
#print("# keypoint matches: ", len(idxs_cur))
Trc = self.estimatePose(f_ref.kpsn[idxs_ref], f_cur.kpsn[idxs_cur])
Tcr = inv_T(Trc) # Tcr w.r.t. ref frame
f_ref.update_pose(np.eye(4))
f_cur.update_pose(Tcr)
# remove outliers from keypoint matches by using the mask computed with inter frame pose estimation
mask_idxs = (self.mask_match.ravel() == 1)
self.num_inliers = sum(mask_idxs)
#print('# keypoint inliers: ', self.num_inliers )
idx_cur_inliers = idxs_cur[mask_idxs]
idx_ref_inliers = idxs_ref[mask_idxs]
# create a temp map for initializing
map = Map()
f_ref.reset_points()
f_cur.reset_points()
#map.add_frame(f_ref)
#map.add_frame(f_cur)
kf_ref = KeyFrame(f_ref)
kf_cur = KeyFrame(f_cur, img_cur)
map.add_keyframe(kf_ref)
map.add_keyframe(kf_cur)
pts3d, mask_pts3d = triangulate_normalized_points(
kf_cur.Tcw, kf_ref.Tcw, kf_cur.kpsn[idx_cur_inliers],
kf_ref.kpsn[idx_ref_inliers])
new_pts_count, mask_points, _ = map.add_points(
pts3d,
mask_pts3d,
kf_cur,
kf_ref,
idx_cur_inliers,
idx_ref_inliers,
img_cur,
do_check=True,
cos_max_parallax=Parameters.kCosMaxParallaxInitializer)
#print("# triangulated points: ", new_pts_count)
if new_pts_count > self.num_min_triangulated_points:
err = map.optimize(verbose=False,
rounds=20,
use_robust_kernel=True)
print("init optimization error^2: %f" % err)
num_map_points = len(map.points)
print("# map points: %d" % num_map_points)
is_ok = num_map_points > self.num_min_triangulated_points
out.pts = pts3d[mask_points]
out.kf_cur = kf_cur
out.idxs_cur = idx_cur_inliers[mask_points]
out.kf_ref = kf_ref
out.idxs_ref = idx_ref_inliers[mask_points]
# set scene median depth to equal desired_median_depth'
desired_median_depth = Parameters.kInitializerDesiredMedianDepth
median_depth = kf_cur.compute_points_median_depth(out.pts)
depth_scale = desired_median_depth / median_depth
print('forcing current median depth ', median_depth, ' to ',
desired_median_depth)
out.pts[:, :3] = out.pts[:, :3] * depth_scale # scale points
tcw = kf_cur.tcw * depth_scale # scale initial baseline
kf_cur.update_translation(tcw)
map.delete()
if is_ok:
Printer.green('Inializer: ok!')
else:
self.num_failures += 1
#Printer.red('Inializer: ko!')
print('|------------')
return out, is_ok
class SLAM(object):
def __init__(self, camera, tracker, grountruth=None):
self.cam = camera
self.map = Map()
Frame.set_tracker(tracker) # set the static field of the class
# camera info
self.W, self.H = camera.width, camera.height
self.K = camera.K
self.Kinv = camera.Kinv
self.D = camera.D # distortion coefficients [k1, k2, p1, p2, k3]
self.stage = SLAMStage.NO_IMAGES_YET
self.intializer = Initializer()
self.cur_R = None # current rotation w.r.t. world frame
self.cur_t = None # current translation w.r.t. world frame
self.num_matched_kps = None # current number of matched keypoints
self.num_inliers = None # current number of matched inliers
self.num_vo_map_points = None # current number of valid VO map points (matched and found valid in current pose optimization)
self.trueX, self.trueY, self.trueZ = None, None, None
self.grountruth = grountruth
self.mask_match = None
self.velocity = None
self.init_history = True
self.poses = [] # history of poses
self.t0_est = None # history of estimated translations
self.t0_gt = None # history of ground truth translations (if available)
self.traj3d_est = [
] # history of estimated translations centered w.r.t. first one
self.traj3d_gt = [
] # history of estimated ground truth translations centered w.r.t. first one
self.timer_verbose = kTimerVerbose # set this to True if you want to print timings
self.timer_main_track = TimerFps('Track',
is_verbose=self.timer_verbose)
self.timer_pose_opt = TimerFps('Pose optimization',
is_verbose=self.timer_verbose)
self.timer_match = TimerFps('Match', is_verbose=self.timer_verbose)
self.timer_pose_est = TimerFps('Pose estimation',
is_verbose=self.timer_verbose)
self.timer_frame = TimerFps('Frame', is_verbose=self.timer_verbose)
self.timer_seach_map = TimerFps('Search Map',
is_verbose=self.timer_verbose)
self.timer_triangulation = TimerFps('Triangulation',
is_verbose=self.timer_verbose)
self.time_local_opt = TimerFps('Local optimization',
is_verbose=self.timer_verbose)
# fit essential matrix E with RANSAC such that: p2.T * E * p1 = 0 where E = [t21]x * R21
# out: [Rrc, trc] (with respect to 'ref' frame)
# N.B.1: trc is estimated up to scale (i.e. the algorithm always returns ||trc||=1, we need a scale in order to recover a translation which is coherent with previous estimated poses)
# N.B.2: this function has problems in the following cases: [see Hartley/Zisserman Book]
# - 'geometrical degenerate correspondences', e.g. all the observed features lie on a plane (the correct model for the correspondences is an homography) or lie a ruled quadric
# - degenerate motions such a pure rotation (a sufficient parallax is required) or an infinitesimal viewpoint change (where the translation is almost zero)
# N.B.3: the five-point algorithm (used for estimating the Essential Matrix) seems to work well in the degenerate planar cases [Five-Point Motion Estimation Made Easy, Hartley]
# N.B.4: as reported above, in case of pure rotation, this algorithm will compute a useless fundamental matrix which cannot be decomposed to return the rotation
def estimate_pose_ess_mat(self, kpn_ref, kpn_cur):
E, self.mask_match = cv2.findEssentialMat(
kpn_cur,
kpn_ref,
focal=1,
pp=(0., 0.),
method=cv2.RANSAC,
prob=kRansacProb,
threshold=kRansacThresholdNormalized)
_, R, t, mask = cv2.recoverPose(E,
kpn_cur,
kpn_ref,
focal=1,
pp=(0., 0.))
return poseRt(R, t.T) # Rrc,trc (cur with respect to 'ref' frame)
def track(self, img, frame_id, pose=None, verts=None):
# check image size is coherent with camera params
print("img.shape ", img.shape)
print("cam ", self.H, " x ", self.W)
assert img.shape[0:2] == (self.H, self.W)
self.timer_main_track.start()
# build current frame
self.timer_frame.start()
f_cur = Frame(self.map, img, self.K, self.Kinv, self.D, des=verts)
self.timer_frame.refresh()
if self.stage == SLAMStage.NO_IMAGES_YET:
# push first frame in the inizializer
self.intializer.init(f_cur)
self.stage = SLAMStage.NOT_INITIALIZED
return # EXIT (jump to second frame)
if self.stage == SLAMStage.NOT_INITIALIZED:
# try to inizialize
initializer_output, is_ok = self.intializer.initialize(f_cur, img)
if is_ok:
f_ref = self.intializer.f_ref
# add the two initialized frames in the map
self.map.add_frame(
f_ref) # add first frame in map and update its id
self.map.add_frame(
f_cur) # add second frame in map and update its id
# add points in map
new_pts_count, _ = self.map.add_points(
initializer_output.points4d, None, f_cur, f_ref,
initializer_output.idx_cur, initializer_output.idx_ref,
img)
Printer.green("map: initialized %d new points" %
(new_pts_count))
self.stage = SLAMStage.OK
return # EXIT (jump to next frame)
f_ref = self.map.frames[-1] # get previous frame in map
self.map.add_frame(f_cur) # add f_cur to map
# udpdate (velocity) motion model (kinematic without damping)
self.velocity = np.dot(f_ref.pose,
np.linalg.inv(self.map.frames[-2].pose))
predicted_pose = np.dot(self.velocity, f_ref.pose)
if kUseMotionModel is True:
print('using motion model')
# set intial guess for current pose optimization
f_cur.pose = predicted_pose.copy()
#f_cur.pose = f_ref.pose.copy() # get the last pose as an initial guess for optimization
if kUseSearchFrameByProjection:
# search frame by projection: match map points observed in f_ref with keypoints of f_cur
print('search frame by projection...')
idx_ref, idx_cur, num_found_map_pts = search_frame_by_projection(
f_ref, f_cur)
print("# found map points in prev frame: %d " %
num_found_map_pts)
else:
self.timer_match.start()
# find keypoint matches between f_cur and f_ref
idx_cur, idx_ref = match_frames(f_cur, f_ref)
self.num_matched_kps = idx_cur.shape[0]
print('# keypoint matches: ', self.num_matched_kps)
self.timer_match.refresh()
else:
print('estimating pose by fitting essential mat')
self.timer_match.start()
# find keypoint matches between f_cur and f_ref
idx_cur, idx_ref = match_frames(f_cur, f_ref)
self.num_matched_kps = idx_cur.shape[0]
print('# keypoint matches: ', self.num_matched_kps)
self.timer_match.refresh()
# N.B.: please, in order to understand the limitations of fitting an essential mat, read the comments of the method self.estimate_pose_ess_mat()
self.timer_pose_est.start()
# estimate inter frame camera motion by using found keypoint matches
Mrc = self.estimate_pose_ess_mat(f_ref.kpsn[idx_ref],
f_cur.kpsn[idx_cur])
Mcr = np.linalg.inv(poseRt(Mrc[:3, :3], Mrc[:3, 3]))
f_cur.pose = np.dot(Mcr, f_ref.pose)
self.timer_pose_est.refresh()
# remove outliers from keypoint matches by using the mask computed with inter frame pose estimation
mask_index = (self.mask_match.ravel() == 1)
self.num_inliers = sum(mask_index)
print('# inliers: ', self.num_inliers)
idx_ref = idx_ref[mask_index]
idx_cur = idx_cur[mask_index]
# if too many outliers reset estimated pose
if self.num_inliers < kNumMinInliersEssentialMat:
f_cur.pose = f_ref.pose.copy(
) # reset estimated pose to previous frame
Printer.red('Essential mat: not enough inliers!')
# set intial guess for current pose optimization:
# keep the estimated rotation and override translation with ref frame translation (we do not have a proper scale for the translation)
f_cur.pose[:, 3] = f_ref.pose[:, 3].copy()
#f_cur.pose[:,3] = predicted_pose[:,3].copy() # or use motion model for translation
# populate f_cur with map points by propagating map point matches of f_ref:
# we use map points observed in f_ref and keypoint matches between f_ref and f_cur
num_found_map_pts_inter_frame = 0
if not kUseSearchFrameByProjection:
for i, idx in enumerate(idx_ref): # iterate over keypoint matches
if f_ref.points[
idx] is not None: # if we have a map point P for i-th matched keypoint in f_ref
f_ref.points[idx].add_observation(
f_cur, idx_cur[i]
) # then P is automatically matched to the i-th matched keypoint in f_cur
num_found_map_pts_inter_frame += 1
print("# matched map points in prev frame: %d " %
num_found_map_pts_inter_frame)
# f_cur pose optimization 1 (here we use first available information coming from first guess of f_cur pose and map points of f_ref matched keypoints )
self.timer_pose_opt.start()
pose_opt_error, pose_is_ok, self.num_vo_map_points = optimizer_g2o.poseOptimization(
f_cur, verbose=False)
print("pose opt err1: %f, ok: %d" % (pose_opt_error, int(pose_is_ok)))
# discard outliers detected in pose optimization (in current frame)
#f_cur.reset_outlier_map_points()
if pose_is_ok is True:
# discard outliers detected in f_cur pose optimization (in current frame)
f_cur.reset_outlier_map_points()
else:
# if current pose optimization failed, reset f_cur pose to f_ref pose
f_cur.pose = f_ref.pose.copy()
self.timer_pose_opt.pause()
# TODO: add recover in case of f_cur pose optimization failure
# now, having a better estimate of f_cur pose, we can find more map point matches:
# find matches between {local map points} (points in the built local map) and {unmatched keypoints of f_cur}
if pose_is_ok is True and not self.map.local_map.is_empty():
self.timer_seach_map.start()
#num_found_map_pts = search_local_frames_by_projection(self.map, f_cur)
num_found_map_pts = search_map_by_projection(
self.map.local_map.points, f_cur) # use the built local map
print("# matched map points in local map: %d " % num_found_map_pts)
self.timer_seach_map.refresh()
# f_cur pose optimization 2 with all the matched map points
self.timer_pose_opt.resume()
pose_opt_error, pose_is_ok, self.num_vo_map_points = optimizer_g2o.poseOptimization(
f_cur, verbose=False)
print("pose opt err2: %f, ok: %d" %
(pose_opt_error, int(pose_is_ok)))
print("# valid matched map points: %d " % self.num_vo_map_points)
# discard outliers detected in pose optimization (in current frame)
if pose_is_ok is True:
f_cur.reset_outlier_map_points()
self.timer_pose_opt.refresh()
if kUseSearchFrameByProjection:
print("search for triangulation with epipolar lines...")
idx_ref, idx_cur, self.num_matched_kps, _ = search_frame_for_triangulation(
f_ref, f_cur, img)
print("# matched keypoints in prev frame: %d " %
self.num_matched_kps)
# if pose is ok, then we try to triangulate the matched keypoints (between f_cur and f_ref) that do not have a corresponding map point
if pose_is_ok is True and len(idx_ref) > 0:
self.timer_triangulation.start()
# TODO: this triangulation should be done from keyframes!
good_pts4d = np.array([
f_cur.points[i] is None for i in idx_cur
]) # matched keypoints of f_cur without a corresponding map point
# do triangulation in global frame
pts4d = triangulate_points(f_cur.pose, f_ref.pose,
f_cur.kpsn[idx_cur],
f_ref.kpsn[idx_ref], good_pts4d)
good_pts4d &= np.abs(pts4d[:, 3]) != 0
#pts4d /= pts4d[:, 3:]
pts4d[good_pts4d] /= pts4d[good_pts4d,
3:] # get homogeneous 3-D coords
new_pts_count, _ = self.map.add_points(pts4d,
good_pts4d,
f_cur,
f_ref,
idx_cur,
idx_ref,
img,
check_parallax=True)
print("# added map points: %d " % (new_pts_count))
self.timer_triangulation.refresh()
# local optimization
self.time_local_opt.start()
err = self.map.locally_optimize(local_window=kLocalWindow)
print("local optimization error: %f" % err)
self.time_local_opt.refresh()
# large window optimization of the map
# TODO: move this in a seperate thread with local mapping
if kUseLargeWindowBA is True and f_cur.id >= parameters.kEveryNumFramesLargeWindowBA and f_cur.id % parameters.kEveryNumFramesLargeWindowBA == 0:
err = self.map.optimize(
local_window=parameters.kLargeWindow) # verbose=True)
Printer.blue("large window optimization error: %f" % err)
print("map: %d points, %d frames" %
(len(self.map.points), len(self.map.frames)))
#self.updateHistory()
self.timer_main_track.refresh()
def updateHistory(self):
f_cur = self.map.frames[-1]
self.cur_R = f_cur.pose[:3, :3].T
self.cur_t = np.dot(-self.cur_R, f_cur.pose[:3, 3])
if (self.init_history is True) and (self.trueX is not None):
self.t0_est = np.array(
[self.cur_t[0], self.cur_t[1],
self.cur_t[2]]) # starting translation
self.t0_gt = np.array([self.trueX, self.trueY,
self.trueZ]) # starting translation
if (self.t0_est is not None) and (self.t0_gt is not None):
p = [
self.cur_t[0] - self.t0_est[0], self.cur_t[1] - self.t0_est[1],
self.cur_t[2] - self.t0_est[2]
] # the estimated traj starts at 0
self.traj3d_est.append(p)
self.traj3d_gt.append([
self.trueX - self.t0_gt[0], self.trueY - self.t0_gt[1],
self.trueZ - self.t0_gt[2]
])
self.poses.append(poseRt(self.cur_R, p))
# get current translation scale from ground-truth if this is set
def getAbsoluteScale(self, frame_id):
if self.grountruth is not None and kUseGroundTruthScale:
self.trueX, self.trueY, self.trueZ, scale = self.grountruth.getPoseAndAbsoluteScale(
frame_id)
return scale
else:
self.trueX = 0
self.trueY = 0
self.trueZ = 0
return 1
def initialize(self, f_cur, img_cur):
# prepare the output
out = InitializerOutput()
is_ok = False
# if too many frames have passed, move the current id_ref forward
if (len(self.frames) - 1) - self.id_ref >= kMaxIdDistBetweenFrames:
self.id_ref = len(self.frames) - 1 # take last frame in the array
self.f_ref = self.frames[self.id_ref]
f_ref = self.f_ref
# append current frame
self.frames.append(f_cur)
# if the current frames do no have enough features exit
if len(f_ref.kps) < kNumMinFeatures or len(
f_cur.kps) < kNumMinFeatures:
Printer.red('Inializer: not enough features!')
return out, is_ok
# find image point matches
idx_cur, idx_ref = match_frames(f_cur, f_ref)
print('├────────')
print('initializing frames ', f_cur.id, ', ', f_ref.id)
Mrc = self.estimatePose(f_ref.kpsn[idx_ref], f_cur.kpsn[idx_cur])
f_cur.pose = np.linalg.inv(poseRt(
Mrc[:3, :3], Mrc[:3, 3])) # [Rcr, tcr] w.r.t. ref frame
# remove outliers
mask_index = [i for i, v in enumerate(self.mask_match) if v > 0]
print('num inliers: ', len(mask_index))
idx_cur_inliers = idx_cur[mask_index]
idx_ref_inliers = idx_ref[mask_index]
# create a temp map for initializing
map = Map()
map.add_frame(f_ref)
map.add_frame(f_cur)
points4d = self.triangulatePoints(f_cur.pose, f_ref.pose,
f_cur.kpsn[idx_cur_inliers],
f_ref.kpsn[idx_ref_inliers])
#pts4d = triangulate(f_cur.pose, f_ref.pose, f_cur.kpsn[idx_cur], f_ref.kpsn[idx_ref])
new_pts_count, mask_points = map.add_points(points4d,
None,
f_cur,
f_ref,
idx_cur_inliers,
idx_ref_inliers,
img_cur,
check_parallax=True)
print("triangulated: %d new points from %d matches" %
(new_pts_count, len(idx_cur)))
err = map.optimize(verbose=False)
print("pose opt err: %f" % err)
#reset points in frames
f_cur.reset_points()
f_ref.reset_points()
num_map_points = len(map.points)
#print("# map points: %d" % num_map_points)
is_ok = num_map_points > kNumMinTriangulatedPoints
out.points4d = points4d[mask_points]
out.f_cur = f_cur
out.idx_cur = idx_cur_inliers[mask_points]
out.f_ref = f_ref
out.idx_ref = idx_ref_inliers[mask_points]
# set median depth to 'desired_median_depth'
desired_median_depth = parameters.kInitializerDesiredMedianDepth
median_depth = f_cur.compute_points_median_depth(out.points4d)
depth_scale = desired_median_depth / median_depth
print('median depth: ', median_depth)
out.points4d = out.points4d * depth_scale # scale points
f_cur.pose[:3,
3] = f_cur.pose[:3,
3] * depth_scale # scale initial baseline
print('├────────')
Printer.green('Inializer: ok!')
return out, is_ok
