def update_internal_state(self, habitat_observation):
rgb, depth = self.rgb_d_from_observation(habitat_observation)
try:
self.slam.process_image_rgbd(rgb, depth, self.cur_time)
self.tracking_is_OK = str(self.slam.get_tracking_state()) == "OK"
except BaseException:
print("Warning!!!! ORBSLAM processing frame error")
self.tracking_is_OK = False
if self.tracking_is_OK:
trajectory_history = np.array(self.slam.get_trajectory_points())
self.pose6D = homogenize_p(
torch.from_numpy(trajectory_history[-1])[1:].view(3, 4).to(
self.device)).view(1, 4, 4)
self.trajectory_history = trajectory_history
current_obstacles = self.mapper(
torch.from_numpy(depth).to(self.device).squeeze(),
self.pose6D).to(self.device)
self.current_obstacles = current_obstacles
self.map2DObstacles = torch.max(
self.map2DObstacles,
current_obstacles.unsqueeze(0).unsqueeze(0),
)
return True
else:
return False
def update_internal_state(self, habitat_observation):
# sets self.obs = observation
super(ORBSLAM2Agent, self).update_internal_state(habitat_observation)
if self.start_location is None:
self.start_location = habitat_observation['gps']
if self.start_rotation is None:
self.start_rotation = habitat_observation['compass']
self.cur_time += self.timestep
rgb, depth = self.rgb_d_from_observation(habitat_observation)
t = time.time()
try:
self.slam.process_image_rgbd(rgb, depth, self.cur_time)
if self.timing:
print(time.time() - t, "ORB_SLAM2")
self.tracking_is_OK = str(self.slam.get_tracking_state()) == "OK"
except BaseException as e:
#print("Warning!!!! ORBSLAM processing frame error")
#print("orbslam error:", e)
self.tracking_is_OK = False
if not self.tracking_is_OK:
#print("\n\n\n\nRESETTING MYSELF BC TRACKING NOT OKAY\n\n\n\n")
#print("SLAM TRACKING STATE:", self.slam.get_tracking_state())
self.reset(
np.append(habitat_observation['gps'],
habitat_observation['compass']))
t = time.time()
#self.set_offset_to_goal(habitat_observation)
if self.tracking_is_OK:
trajectory_history = np.array(self.slam.get_trajectory_points())
self.pose6D = homogenize_p(
torch.from_numpy(trajectory_history[-1])[1:].view(3, 4).to(
self.device)).view(1, 4, 4)
self.trajectory_history = trajectory_history
if len(self.position_history) > 1:
previous_step = get_distance(
self.pose6D.view(4, 4),
torch.from_numpy(self.position_history[-1]).view(4, 4).to(
self.device),
)
if self.action_history[-1] == HabitatSimActions.MOVE_FORWARD:
self.unseen_obstacle = (
previous_step.item() <= 0.001
) # hardcoded threshold for not moving
current_obstacles = self.mapper(
torch.from_numpy(depth).to(self.device).squeeze(),
self.pose6D).to(self.device)
self.current_obstacles = current_obstacles
self.map2DObstacles = torch.max(
self.map2DObstacles,
current_obstacles.unsqueeze(0).unsqueeze(0))
if self.timing:
print(time.time() - t, "Mapping")
return True
def update_internal_state(self, habitat_observation):
super(ORBSLAM2AgentAimas,
self).update_internal_state(habitat_observation)
self.cur_time += self.timestep
rgb, depth = self.rgb_d_from_observation(habitat_observation)
t = time.time()
try:
self.slam.process_image_rgbd(rgb, depth, self.cur_time)
if self.timing:
print(time.time() - t, "ORB_SLAM2")
self.tracking_is_OK = str(self.slam.get_tracking_state()) == "OK"
except BaseException:
print("Warning!!!! ORBSLAM processing frame error")
self.tracking_is_OK = False
if not self.tracking_is_OK:
self.reset()
t = time.time()
self.set_offset_to_goal(habitat_observation)
if self.tracking_is_OK:
trajectory_history = np.array(self.slam.get_trajectory_points())
self.pose6D = homogenize_p(
torch.from_numpy(trajectory_history[-1])[1:].view(3, 4).to(
self.device)).view(1, 4, 4)
self.trajectory_history = trajectory_history
if len(self.position_history) > 1:
previous_step = get_distance(
self.pose6D.view(4, 4),
torch.from_numpy(self.position_history[-1]).view(4, 4).to(
self.device),
)
if self.action_history[-1] == SimulatorActions.MOVE_FORWARD:
self.unseen_obstacle = (
previous_step.item() <= 0.001
) # hardcoded threshold for not moving
current_obstacles = self.mapper(
torch.from_numpy(depth).to(self.device).squeeze(),
self.pose6D).to(self.device)
self.current_obstacles = current_obstacles
self.map2DObstacles = torch.max(
self.map2DObstacles,
current_obstacles.unsqueeze(0).unsqueeze(0))
if self.timing:
print(time.time() - t, "Mapping")
return True