def run(self):
# Create an RMHC SLAM object with a laser model and optional robot model
self.slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
self.viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
command_handlers = {
Message.lidar_data: self.update_map,
Message.get_map: self.process_get_map_command,
Message.reset_map: self.process_reset_map_command,
}
self.message_bus.subscribe(topic = self.lidar_updates_topic_name, subscriber=self.name)
self.config.log.info("slam subscribed to %s" % self.lidar_updates_topic_name)
while True:
msg = self.message_bus.receive(self.name)
handler = command_handlers.get(msg.cmd, self.handle_invalid_command_id)
handler(msg)
return
def main():
# Connect to Lidar unit
lidar = Lidar(LIDAR_DEVICE)
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
# Initialize an empty trajectory
trajectory = []
# Initialize empty map
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
# Create an iterator to collect scan data from the RPLidar
iterator = lidar.iter_scans()
# We will use these to store previous scan in case current scan is inadequate
previous_distances = None
previous_angles = None
# First scan is crap, so ignore it
next(iterator)
while True:
# Extract (quality, angle, distance) triples from current scan
items = [item for item in next(iterator)]
# Extract distances and angles from triples
distances = [item[2] for item in items]
print(distances)
angles = [item[1] for item in items]
# Update SLAM with current Lidar scan and scan angles if adequate
if len(distances) > MIN_SAMPLES:
slam.update(distances, scan_angles_degrees=angles)
previous_distances = distances.copy()
previous_angles = angles.copy()
# If not adequate, use previous
elif previous_distances is not None:
slam.update(previous_distances,
scan_angles_degrees=previous_angles)
# Get current robot position
x, y, theta = slam.getpos()
# Get current map bytes as grayscale
slam.getmap(mapbytes)
# Display map and robot pose, exiting gracefully if user closes it
if not viz.display(x / 1000., y / 1000., theta, mapbytes):
exit(0)
# Shut down the lidar connection
lidar.stop()
lidar.disconnect()
def build(self,
laserModel,
map_size_pix,
map_size_m,
initial_position=(-1, -1)):
slam = RMHC_SLAM(LaserModel(), self.MAP_SIZE_PIXELS,
self.MAP_SIZE_METERS)
if not initial_position == (-1, -1):
slam.position = pybreezyslam.Position(initial_position[0] * 10,
initial_position[0] * 10, 0)
return slam
def __init__(self):
self.flag = 0
self.pause = 0
self.lidar = Lidar(LIDAR_DEVICE)
self.slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
#self.viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
self.trajectory = []
self.mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
self.iterator = self.lidar.iter_scans()
self.previous_distances = None
self.x = 0.0
self.y = 0.0
self.theta = 0.0
def __init__(self):
# Connect to Lidar unit
self.lidar = lidarReader('/dev/mylidar', 115200)
self.mover = MotorController()
# Create an RMHC SLAM object with a laser model and optional robot model
self.slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
self.viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
# Initialize empty map
self.mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
self.left_distance_table = np.zeros(80)
self.right_distance_table = np.zeros(80)
self.thread = threading.Thread(target=self.navigate, args=())
self.thread.start()
def __init__(self):
super().__init__('slam_node')
# Create second subscription (SLAM)
self.subscription2 = self.create_subscription(
LidarData, 'lidar_data', self.listener_callback_lidar, 100)
self.subscription2 # prevent unused variable warning
# Create third subscription (Motor speeds to compute robot pose change)
self.subscription3 = self.create_subscription(
MotorsSpeed, 'motors_speed', self.listener_callback_motorsspeed,
1000)
self.subscription3 # prevent unused variable warning
# create subscription to control map_quality
self.slam_control_sub = self.create_subscription(
String, 'slam_control', self.slam_control_callback, 1000)
# Create publisher for the position of the robot, and for the map
self.publisher_position = self.create_publisher(
Position, 'robot_pos', 10)
self.publisher_map = self.create_publisher(Map, 'world_map', 10)
# Initialize parameters for slam
laser = LaserModel(detection_margin=DETECTION_MARGIN,
offset_mm=OFFSET_MM)
self.slam = RMHC_SLAM(laser,
MAP_SIZE_PIXELS,
MAP_SIZE_METERS,
map_quality=MAP_QUALITY,
hole_width_mm=OBSTACLE_WIDTH_MM,
x0_mm=X0,
y0_mm=Y0,
theta0=0,
sigma_xy_mm=DEFAULT_SIGMA_XY_MM)
self.trajectory = []
self.mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
self.previous_distances = None
self.previous_angles = None
self.robot_pose_change = np.array([0.0, 0.0, 0.0])
self.is_map_quality_high = True
# DEBUG parameters
self.map_index = 0
self.previous_pos = (0, 0, 0)
self.s = time.time()
print("SLAM is starting")
def __init__(self, visualize):
super().__init__('cont')
self.visualize = visualize
self.slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
if self.visualize:
self.viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
self.subscriber = self.create_subscription(LaserScan, '/scan',
self.lidarCallback, 10)
self.mapPublisher = self.create_publisher(OccupancyGrid, '/map', 1)
self.locPublisher = self.create_publisher(Pose, '/pose', 1)
print("Setup Finished")
self.previous_distances = None
self.previous_angles = None
self.mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
self.i = True
def __init__(self, station, robot_pos):
self.station = station
self.robot_pos = robot_pos
# SLAM
self.MAP_SIZE_PIXELS = 500
self.MAP_SIZE_METERS = 10
self.MIN_SAMPLES = 200
self.lidar = Lidar('/dev/ttyUSB0')
self.initial_pos_cm = (0, 0)
self.slam = SlamFactory.build(LaserModel(), self.MAP_SIZE_PIXELS,
self.MAP_SIZE_METERS, initial_pos_cm)
self.mapbytes = bytearray(self.MAP_SIZE_PIXELS * self.MAP_SIZE_PIXELS)
self.iterator = self.lidar.iter_scans()
self.previous_distances = None
self.previous_angles = None
next(self.iterator)
def main():
lidar = Lidar(device='/dev/ttyACM0')
slam = RMHC_SLAM(LaserModel(), map_size_pixels=MAP_SIZE_PIXELS, map_size_meters=MAP_SIZE_METERS)
display = SlamShow(MAP_SIZE_PIXELS, MAP_SIZE_METERS*1000/MAP_SIZE_PIXELS, 'SLAM')
#image_thread = threading.Thread(target=save_image, args=[display])
#image_thread.start()
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
while True:
slam.update(lidar.getScan())
x, y, theta = slam.getpos()
#print(x, y, theta)
slam.getmap(mapbytes)
display.displayMap(mapbytes)
display.setPose(x, y, theta)
#display.save_image()
display.save_pgm(mapbytes)
if not display.refresh():
exit(0)
def __init__(self, messagequeue):
# Connect to Lidar unit
# when initializing the robot object, we first open lidar and start
# reading data from the lidar
self.lidar = lidarReader('/dev/mylidar', 115200)
self.mover = MotorController()
# Create an RMHC SLAM object with a laser model and optional robot model
self.slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
self.viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
# Initialize empty map
self.mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
self.messages = messagequeue
# All the functions should run in the same process so that the variable can be shared
self.thread = threading.Thread(target=self.navigate, args=())
# the main-thread will exit without checking the sub-thread at the end of the main thread.
# At the same time, all sub-threads with the daemon value of True will end with the main thread, regardless of whether the operation is completed.
self.thread.daemon = True
self.navigating = True
self.thread.start()
def setup(self):
GPIO.output(23, GPIO.HIGH)
sleep(1)
self.lidar = RPLidar(self.port)
self.lidar.connect()
self.lidar.start_motor()
self.slam = RMHC_SLAM(LaserModel(),
MAP_SIZE_PIXELS,
MAP_SIZE_METERS,
map_quality=1,
max_search_iter=50)
self.mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
success = False
while success is not True:
try:
self.iterator = self.lidar.iter_scans()
next(self.iterator) #first scan is shit
success = True
except Exception as e:
print('retrying')
print(e)
sleep(0.5)
async def main():
async with websockets.connect(SERVER_URL,
max_size=None,
max_queue=None,
ping_interval=None,
ping_timeout=None) as websocket:
# Connect to Lidar unit
lidar = Lidar(LIDAR_DEVICE)
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Initialize an empty trajectory
trajectory = []
# Initialize empty map
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
# Create an iterator to collect scan data from the RPLidar
iterator = lidar.iter_scans()
# We will use these to store previous scan in case current scan is inadequate
previous_distances = None
previous_angles = None
# First scan is crap, so ignore it
next(iterator)
while True:
# Extract (quality, angle, distance) triples from current scan
items = [item for item in next(iterator)]
# Extract distances and angles from triples
distances = [item[2] for item in items]
angles = [item[1] for item in items]
# Update SLAM with current Lidar scan and scan angles if adequate
if len(distances) > MIN_SAMPLES:
slam.update(distances, scan_angles_degrees=angles)
previous_distances = distances.copy()
previous_angles = angles.copy()
# If not adequate, use previous
elif previous_distances is not None:
slam.update(previous_distances, scan_angles_degrees=previous_angles)
# Get current robot position
x, y, theta = slam.getpos()
# Get current map bytes as grayscale
slam.getmap(mapbytes)
while True:
try:
await websocket.send(struct.pack(STRUCT_FORMAT, x, y, theta, mapbytes))
except:
else:
break
# Shut down the lidar connection
lidar.stop()
lidar.disconnect()
# save to a png file
fig.savefig(image_filename)
return self._refresh()
# image file
image_filename = '/home/ubuntu/RCWeb/dash'
HOST, PORT = "192.168.0.18", 50007
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
# Initialize an empty trajectory
trajectory = []
# Initialize empty map
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
# We will use these to store previous scan in case current scan is inadequate
previous_distances = None
previous_angles = None
# Pin Definitons:
img_dict[0] = cv2.flip(cv2.imread('icons8-street-view-50.jpg'), 0)
img_dict[1] = cv2.flip(cv2.imread('icons8-staircase-50.jpg'), 0)
img_dict[2] = cv2.flip(cv2.imread('icons8-fire-extinguisher-50.jpg'), 0)
img_dict[3] = cv2.flip(cv2.imread('icons8-exit-sign-50.jpg'), 0)
img_dict[4] = cv2.flip(cv2.imread('icons8-circuit-50.jpg'), 0)
img_dict[5] = cv2.flip(cv2.imread('icons8-elevator-50.jpg'), 0)
img_dict[6] = cv2.flip(cv2.imread('icons8-test-tube-50.jpg'), 0)
img_dict[7] = cv2.flip(cv2.imread('icons8-biohazard-26.jpg'), 0)
img_dict[8] = cv2.flip(cv2.imread('icons8-radio-active-50.jpg'), 0)
img_dict[9] = cv2.flip(cv2.imread('icons8-door-opened-50.jpg'), 0)
temp = np.load('scan.npy')
print(temp)
# Connect to Lidar and initialize variables
lidar = Lidar(args.device)
slam = RMHC_SLAM(LaserModel(), args.pixelmapsize, args.metermapsize)
display = SlamShow(args.pixelmapsize,
args.metermapsize * 1000 / args.pixelmapsize, 'SLAM')
trajectory = []
mapbytes = bytearray(args.pixelmapsize * args.pixelmapsize)
iterator = lidar.iter_scans()
previous_distances = None
previous_angles = None
next(iterator)
prev_items = None
labels_dict = {}
np.save("label.npy", np.array([]))
### Primary SLAM Loop
while True:
def init_slam(self):
self.MAP_SIZE_PIXELS = cfg.MAP_SIZE_PIXELS
self.MAP_SIZE_METERS = cfg.MAP_SIZE_METERS
self.slam = RMHC_SLAM(LaserModel(), self.MAP_SIZE_PIXELS,
self.MAP_SIZE_METERS)
self.mapbytes = bytearray(self.MAP_SIZE_PIXELS * self.MAP_SIZE_PIXELS)
def __init__(self,
mode='xbox',
initialize_imu=True,
imu_bus=0,
initialize_camera=False,
initialize_lidar=False,
slam_map_meters=15,
slam_map_pixels=1000,
max_speed_limit=5,
max_duty_cycle=0.2,
rc_control='VESC_CONTROL',
rc_communication='/dev/ttyTHS1'):
"""
:param mode: Control mode for the vehicle. Possible values are:
>- JetsonEV.xbox_mode
>- JetsonEV.xbox_direct_mode
>- JetsonEV.xbox_forwarding_mode
>See [here](./modes.html) for more details on each mode.
:param initialize_imu: Whether or not to initialize imu
:param imu_bus: I2C bus number to communicate with the IMU
:param initialize_camera: Whether or not to initialize camera
:param initialize_lidar: Whether or not to initialize rplidar
:param slam_map_meters: The distance in meters to use for the SLAM map size. A square map is
made using this distance for each side.
:param slam_map_pixels: The SLAM square map size in pixels. This controls the resolution of the map.
:param max_speed_limit: Max speed in m/s when using speed controller.
:param max_duty_cycle: Max duty cycle to send when using direct duty cycle control.
:param rc_control: Type of hardware control for rc components (motor and steering servo). Can be either
JetsonEV.VESC_CONTROL or JetsonEV.ARDUINO_CONTROL.
:param rc_communication: Port or bus to use for rc_control. If using VESC, this should point to the UART port.
If using USB for UART control, set rc_communication = 'USB' (linux only).
If rc_control=JetsonEV.ARDUINO, this should be an integer for the I2C bus number.
* * *
"""
JetsonEV.__running_car__ = self
self.rc_control = rc_control
self._timed_tasks = []
"""private list to hold all existing TimedTask objects"""
self._output_sockets = []
"""private list to hold all existing output TCP sockets objects"""
self.max_speed_limit = max_speed_limit
self.duty_cycle_limit = max_duty_cycle
self._speed = [0]
self._speed_lock = threading.Lock()
'''************** Initiate Steering and Motor ****************'''
try:
self._initialize_rc_control(rc_communication)
except OSError as e:
print(e)
'''*********************************************************'''
'''****************** Initiate Camera **********************'''
self.camera = None
if initialize_camera:
print('initiating Camera... ', end='')
try:
camera = Camera(flip=2)
self._latest_frame = [0]
self._latest_frame_lock = threading.Lock()
self.camera_socket = SendSocket(tcp_port=JetsonEV.CAMERA_PORT,
tcp_ip=ip_address)
self._output_sockets.append(self.camera_socket)
def update_frame(frame):
self.latest_frame = frame
self.camera_socket.send_data(frame)
# wrap the object in a TimedTask set to update at fixed rate
self.camera = self.add_timed_task(
calling_function=camera.read,
object_to_wrap=camera,
forwarding_function=update_frame,
sampling_rate=120)
except Exception as e:
print(e)
print('success')
'''*********************************************************'''
'''******************* Initiate IMU ************************'''
self.imu: TimedTask
if initialize_imu:
try:
imu = MPU6050(bus=imu_bus,
device_addr=0x68) # create the imu device
print('Successfully connected to IMU')
self._imu_values = [0]
self._imu_values_lock = threading.Lock()
# wrap the object in a TimedTask set to update at fixed rate
self.imu = self.add_timed_task(
calling_function=self._get_imu_values,
object_to_wrap=imu,
sampling_rate=50)
self.imu_socket = SendSocket(tcp_port=JetsonEV.IMU_PORT,
tcp_ip=ip_address)
self._output_sockets.append(self.imu_socket)
imu_config_dict = get_calibration()
self._imu_rotation = np.array(
imu_config_dict['rotation_matrix'])
self._gravity_offset = imu_config_dict['gravity']
self._gyro_offset = np.array(imu_config_dict['gyro_offsets'])
except Exception as e:
print(e)
print('ERROR: Could not connect to IMU')
self.imu = None
if hasattr(self, 'imu_socket'):
self._output_sockets.remove(self.imu_socket)
'''*********************************************************'''
'''****************** Initiate Lidar ***********************'''
self.lidar = None
self._theta_lock = threading.Lock()
self._theta = 0
self._x_lock = threading.Lock()
self._x = 0
self._y_lock = threading.Lock()
self._y = 0
self._map_lock = threading.Lock()
self._map = bytearray(slam_map_pixels * slam_map_pixels)
self._breezyslam = RMHC_SLAM(LaserModel(),
map_size_meters=slam_map_meters,
map_size_pixels=slam_map_pixels,
hole_width_mm=500)
if initialize_lidar:
started = threading.Event(
) # threading event to watch if lidar started
# This is done to prevent the script from locking up if the lidar needs to be restarted
def check_lidar():
start_time = time.time()
while not started.isSet():
time.sleep(0.2)
if time.time() - start_time > 10:
os.kill(os.getpid(), signal.SIGTERM)
raise Exception('Unable to start Lidar.')
initialize_thread = threading.Thread(target=check_lidar)
initialize_thread.start()
try:
print('connecting to lidar...', end='')
lidar = RPLidar(scan_mode=0)
started.set()
try:
initialize_thread.join()
except Exception as e:
raise e
except Exception as e:
print(e)
print('ERROR: Unable to connect to lidar sensor.')
os.kill(os.getpid(), signal.SIGTERM)
self._latest_lidar_scan = lidar.get_scan_as_xy(filter_quality=True)
self._latest_lidar_scan_lock = threading.Lock()
print('success')
self.lidar_socket = SendSocket(tcp_port=JetsonEV.LIDAR_PORT,
tcp_ip=ip_address)
self._output_sockets.append(self.lidar_socket)
self.slam_socket = SendSocket(tcp_port=JetsonEV.SLAM_PORT,
tcp_ip=ip_address)
self._output_sockets.append(self.slam_socket)
self._update_map = [True]
self._save_criteria_dist = 100 # must move ** mm before updating map
self._save_criteria_angle = 20 # must rotate more than ** deg before updating map
def update_scan(scan):
scan = np.array(scan)
self.latest_lidar_scan = scan
self._breezyslam.update(scan[:, 1].tolist(),
scan_angles_degrees=scan[:,
0].tolist(),
should_update_map=self._update_map[0])
x, y, theta = self._breezyslam.getpos()
dist = np.sqrt((x - self.x)**2 + (y - self.y)**2)
del_theta = abs(theta - self.theta)
if dist > self._save_criteria_dist or del_theta > self._save_criteria_angle:
self._update_map[0] = True
else:
self._update_map[0] = False
self.x = x
self.y = y
self.theta = theta
with self._map_lock:
self._breezyslam.getmap(self._map)
self.lidar_socket.send_data(scan)
self.slam_socket.send_data({
'x':
self.x,
'y':
self.y,
'theta':
self.theta,
'map':
np.frombuffer(self.map, dtype=np.uint8),
'meters':
slam_map_meters,
'pixels':
slam_map_pixels
})
self.lidar = self.add_timed_task(
calling_function=lidar.get_scan_as_vectors,
calling_func_args=[True],
object_to_wrap=lidar,
forwarding_function=update_scan,
sampling_rate=5)
print("initializing SLAM map")
for _ in range(10):
scan = np.array(lidar.get_scan_as_vectors(True))
self._breezyslam.update(scan[:, 1].tolist(),
scan_angles_degrees=scan[:,
0].tolist(),
should_update_map=True)
time.sleep(0.05)
'''*********************************************************'''
'''************ Initiate XBOX Controller *******************'''
self.xbox_controller: Xbox360Controller
self._initialize_xbox_controller(mode)
'''*********************************************************'''
'''***************** Setup speed control *******************'''
self._desired_speed = [0]
self.last_error = 0
self._current_duty = 0
self._integrated_speed_error = 0
self._desired_speed_lock = threading.Lock()
self._speed_control_task = TimedTask(
calling_function=self._speed_control, sampling_rate=20)
if mode in [JetsonEV.xbox_mode]:
# define an in between function to convert percentage from xbox controller to speed using max speed limit.
def percent_to_speed(percent):
self.set_motor_speed(percent * self.max_speed_limit)
self._xbox_speed_func = percent_to_speed
self.duty_cycle_limit = 1
self.start_speed_control()
elif mode in [JetsonEV.xbox_direct_mode]:
self._xbox_speed_func = self.set_motor_percent
'''*********************************************************'''
print('Starting Vehicle...', end='')
self.start_sockets()
self.start_all_tasks()
print('running.')
import paho.mqtt.client as mqtt
import math
import matplotlib.pyplot as plt
import numpy as np
import time
from breezyslam.algorithms import RMHC_SLAM
from breezyslam.sensors import RPLidarA1 as LaserModel
from PIL import Image
MAP_SIZE_PIXELS = 300
MAP_SIZE_METERS = 50
MIN_SAMPLES = 200
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS, random_seed=1)
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
Dist = {}
def mm2pix(mm):
return int(mm / (MAP_SIZE_METERS * 1000. / MAP_SIZE_PIXELS))
def on_connect(self, client, userdata, rc):
print("MQTT Connected.")
self.subscribe("/ldb01/mapdata")
def on_message(client, userdata, msg):
# print("Messsage")
def start_simulation(self):
self.set_motor_speed(0.8, 0)
(errorCode, detectionState, detectedPoint, detectedObjectHandle,
detectedSurfaceNormalVector) = vrep.simxReadProximitySensor(
self.client_id, self.proximity_handle, vrep.simx_opmode_streaming)
e, data = vrep.simxGetStringSignal(self.client_id, "lidarMeasuredData",
vrep.simx_opmode_streaming)
self.check_error_code(e, "simxGetStringSignal lidar error")
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
# Initialize empty map
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
res, v0 = vrep.simxGetObjectHandle(self.client_id, 'Vision_sensor',
vrep.simx_opmode_oneshot_wait)
res, resolution, image = vrep.simxGetVisionSensorImage(
self.client_id, v0, 0, vrep.simx_opmode_streaming)
while vrep.simxGetConnectionId(self.client_id) != -1:
is_detected, distance, vector = self.get_proximity_data()
if is_detected:
print("distance: {} {}".format(distance, vector))
e, lidar_data, dist_data = self.get_lidar_data()
point_data_len = len(lidar_data)
dist_data_len = len(dist_data)
dist_data = dist_data[::-1]
print("points len: {}; dist len: {}".format(
point_data_len, dist_data_len))
self.draw_lidar_data(dist_data[0:-2])
# Update SLAM , with current Lidar scan
slam.update(dist_data[0:-2])
# Get current robot position
x, y, theta = slam.getpos()
print("x: {}; y: {}; theta: {}".format(x, y, theta))
# Get current map bytes as grayscale
slam.getmap(mapbytes)
# Display map and robot pose, exiting gracefully if user closes it
if not viz.display(x / 1000., y / 1000., theta, mapbytes):
exit(0)
err, resolution, image = vrep.simxGetVisionSensorImage(
self.client_id, v0, 0, vrep.simx_opmode_buffer)
if err == vrep.simx_return_ok:
img = np.array(image, dtype=np.uint8)
img.resize([resolution[1], resolution[0], 3])
fimg = cv2.flip(img, 0)
cv2.imshow('vision sensor', fimg)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
elif err == vrep.simx_return_novalue_flag:
print("no image yet")
pass
else:
print(err)
simulationTime = vrep.simxGetLastCmdTime(self.client_id)
time.sleep(0.1)
vrep.simxFinish(self.client_id)
def slam(currentProgram):
trajectory = []
# Connect to Lidar unit
try:
lidar = Lidar(PORT0)
currentProgram.roombaPort = PORT1
iterator = lidar.iter_scans(1000)
lidar.stop()
next(iterator)
print("Lidar 0, Roomba 1")
except:
print("Roomba 0, Lidar 1")
lidar.stop()
lidar.disconnect()
lidar = Lidar(PORT1)
currentProgram.roombaPort = PORT0
iterator = lidar.iter_scans(1000)
lidar.stop()
next(iterator)
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
trajectory = []
previous_distances = None
previous_angles = None
# start time
start_time = time.time()
prevTime = start_time
trigger_start = -100
while (not currentProgram.stop):
SLAMvel = currentProgram.SLAMvals[0]
SLAMrot = currentProgram.SLAMvals[1]
# Extract (quality, angle, distance) triples from current scan
items = [item for item in next(iterator)]
# Extract distances and angles from triples
distances = [item[2] for item in items]
angles = [item[1] for item in items]
l = list(zip(angles,distances))
filtered = list(filter(lambda e: (e[0]>=45 and e[0]<=135) and e[1]<300 , l))
# s = sorted(l, key = lambda e: e[0])
if (len(filtered) > constants.POINTS_THRESHOLD) and (time.time()-trigger_start >5):
currentProgram.programStatus = constants.Status.LIDAR_OBSTACLE
topleft = list(filter(lambda e: (e[0]>=105 and e[0]<=135) , filtered))
front = list(filter(lambda e: (e[0]>=75 and e[0]<=105) , filtered))
topright = list(filter(lambda e: (e[0]>=45 and e[0]<=75) , filtered))
if (len(topleft) > 2):
currentProgram.obstacleLocation[0] = 1
if (len(front) > 2):
currentProgram.obstacleLocation[1] = 1
if (len(topright) > 2):
currentProgram.obstacleLocation[2] = 1
trigger_start = time.time()
# Update SLAM with current Lidar scan and scan angles if adequate
if len(distances) > MIN_SAMPLES:
# print("using speeds ", SLAMvel, SLAMrot)
dt = time.time() - prevTime
slam.update(distances, pose_change=(
(SLAMvel*dt, SLAMrot*dt, dt)), scan_angles_degrees=angles)
prevTime = time.time()
previous_distances = copy.copy(distances)
previous_angles = copy.copy(angles)
# print("updated - if")
# If not adequate, use previous
elif previous_distances is not None:
# print("using speeds ", SLAMvel, SLAMrot)
dt = time.time() - prevTime
slam.update(previous_distances, pose_change=(
(SLAMvel*dt, SLAMrot*dt, dt)), scan_angles_degrees=previous_angles)
prevTime = time.time()
# print("updated - else")
# Get current robot position
x, y, theta = slam.getpos()
[x_pix, y_pix] = [mm2pix(x), mm2pix(y)]
currentProgram.robot_pos = [
y_pix // constants.CHUNK_SIZE, x_pix // constants.CHUNK_SIZE]
# print("robot_pos - ",x_pix // constants.CHUNK_SIZE,y_pix // constants.CHUNK_SIZE, theta)
# Get current map bytes as grayscale
slam.getmap(currentProgram.mapbytes)
# Shut down the lidar connection
pgm_save('ok.pgm', currentProgram.mapbytes,
(MAP_SIZE_PIXELS, MAP_SIZE_PIXELS))
lidar.stop()
lidar.disconnect()
async def main():
#---------------------------------------
# Setup Lidar unit and SLAM
#---------------------------------------
# Connect to Lidar unit
lidar = nanoLidar.nanoLidar(host)
# get firmware info
print("Lidar Version:")
print(lidar.getInfo())
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
display = SlamShow(MAP_SIZE_PIXELS,
MAP_SIZE_METERS * 1000 / MAP_SIZE_PIXELS, 'SLAM')
# Initialize an empty trajectory
trajectory = []
# Initialize empty map
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
#---------------------------------------
# Setup Robot unit
#---------------------------------------
# Connect to robot unit
robot = robotPosition.robotPosition(host)
robot.powerOn()
# get firmware info
print("Chassis Version:")
print(robot.getInfo())
#---------------------------------------
# main loop
#---------------------------------------
run = True
while run:
# Update SLAM with current Lidar scan, using first element of (scan, quality) pairs
slam.update([pair[0] for pair in lidar.getScan()])
# Get current robot position
x, y, theta = slam.getpos()
# Get current map bytes as grayscale
slam.getmap(mapbytes)
display.displayMap(mapbytes)
display.setPose(x, y, theta)
# Exit on ESCape
key = display.refresh()
if key != None and (key & 0x1A):
run = False
# Movement control
distance = int(input("Distance [mm]: "))
angle = int(input("Turn angle [degrees]: "))
(xp, yp, theta_deg, theta_rad) = robot.setPosition(distance, angle)
if int(input("Carry on (1/0): ")) == 0:
run = False
# power off stepper motors
robot.powerOff()
def slamThread():
global SLAMrot
global SLAMvel
# Connect to Lidar unit
lidar = Lidar(LIDAR_DEVICE)
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(), MAP_SIZE_PIXELS, MAP_SIZE_METERS)
# Set up a SLAM display
viz = MapVisualizer(MAP_SIZE_PIXELS, MAP_SIZE_METERS, 'SLAM')
# Initialize an empty trajectory
trajectory = []
# Initialize empty map
mapbytes = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
# Create an iterator to collect scan data from the RPLidar
iterator = lidar.iter_scans()
# We will use these to store previous scan in case current scan is inadequate
previous_distances = None
previous_angles = None
# First scan is crap, so ignore it
next(iterator)
# start time
start_time = time.time()
prevTime = start_time
print("start")
while True:
# Extract (quality, angle, distance) triples from current scan
items = [item for item in next(iterator)]
# Extract distances and angles from triples
distances = [item[2] for item in items]
angles = [item[1] for item in items]
# Update SLAM with current Lidar scan and scan angles if adequate
if len(distances) > MIN_SAMPLES:
slam.update(distances, pose_change = ( (SLAMvel, SLAMrot, time.time() - prevTime)),scan_angles_degrees=angles)
prevTime = time.time()
previous_distances = copy.copy(distances)
previous_angles = copy.copy(angles)
print("updated - if")
print((SLAMvel, SLAMrot, time.time() - prevTime))
# If not adequate, use previous
elif previous_distances is not None:
slam.update(previous_distances, pose_change = ( (SLAMvel, SLAMrot, time.time() - prevTime)),scan_angles_degrees=previous_angles)
prevTime = time.time()
print("updated - else")
print((SLAMvel, SLAMrot, time.time() - prevTime))
# Get current robot position
x, y, theta = slam.getpos()
# Add new position to trajectory
trajectory.append((x, y))
# Get current map bytes as grayscale
slam.getmap(mapbytes)
if(time.time() - start_time > 30):
# Put trajectory into map as black pixels
for coords in trajectory:
x_mm, y_mm = coords
x_pix = mm2pix(x_mm)
y_pix = mm2pix(y_mm)
mapbytes[y_pix * MAP_SIZE_PIXELS + x_pix] = 0;
pgm_save('ok.pgm', mapbytes, (MAP_SIZE_PIXELS, MAP_SIZE_PIXELS))
exit(0)
def run_slam(LIDAR_DEVICE, MAP_SIZE_PIXELS, MAP_SIZE_METERS, mapbytes,
posbytes, odometry, command, state, STATES):
MIN_SAMPLES = 200
# Connect to Lidar unit
lidar = start_lidar(LIDAR_DEVICE)
# Create an RMHC SLAM object with a laser model and optional robot model
slam = RMHC_SLAM(LaserModel(),
MAP_SIZE_PIXELS,
MAP_SIZE_METERS,
map_quality=_DEFAULT_MAP_QUALITY,
hole_width_mm=_DEFAULT_HOLE_WIDTH_MM,
random_seed=None,
sigma_xy_mm=_DEFAULT_SIGMA_XY_MM,
sigma_theta_degrees=_DEFAULT_SIGMA_THETA_DEGREES,
max_search_iter=_DEFAULT_MAX_SEARCH_ITER)
# Initialize empty map
mapbytes_ = bytearray(MAP_SIZE_PIXELS * MAP_SIZE_PIXELS)
# Send command to start scan
lidar.start_scan()
# We will use these to store previous scan in case current scan is inadequate
previous_distances = None
previous_angles = None
pose_change = (0, 0, 0)
pose_change_find = (0, 0, 0)
iter_times = 1
while command[0] != b'qa':
# read a scan
items = lidar.scan()
# Extract distances and angles from triples
distances = [item[2] for item in items]
angles = [item[1] for item in items]
if state[0] == STATES['stop']:
if command[0] == b'lm':
mapbytes_find = mapbytes
pose_change_find = find_slam_pos(lidar, MAP_SIZE_PIXELS,
MAP_SIZE_METERS,
mapbytes_find,
pose_change_find,
iter_times / 2.0, slam)
iter_times = iter_times + 1.0
print(pose_change_find)
pose_change = pose_change_find
pose_change_find = (0, 0, 0)
'''
items = lidar.scan()
# Extract distances and angles from triples
distances = [item[2] for item in items]
angles = [item[1] for item in items]
slam.update(distances, pose_change, scan_angles_degrees=angles)
'''
if (iter_times > 10):
command[0] = b'w'
print(slam)
slam.setmap(mapbytes_find)
pose_change = pose_change_find
pose_change_find = (0, 0, 0)
print('ennnnnnnnnnnnnnnnnnd')
print(pose_change)
iter_times = 1
else:
# Update SLAM with current Lidar scan and scan angles if adequate
if len(distances) > MIN_SAMPLES:
slam.update(distances, pose_change, scan_angles_degrees=angles)
previous_distances = distances.copy()
previous_angles = angles.copy()
# If not adequate, use previous
elif previous_distances is not None:
slam.update(previous_distances,
pose_change,
scan_angles_degrees=previous_angles)
#pass the value into point_cal function
global point_a, point_b
global current_theta_o
# Get current robot position
x, y, theta = slam.getpos()
current_theta_o = theta
point_a = x - 5000
point_b = y - 5000
# Calculate robot position in the map
xbytes = int(x / 1000. / MAP_SIZE_METERS * MAP_SIZE_PIXELS)
ybytes = int(y / 1000. / MAP_SIZE_METERS * MAP_SIZE_PIXELS)
# Update robot position
posbytes[xbytes + ybytes * MAP_SIZE_PIXELS] = 255
#print('LIDAR::', command[0], x,y,theta)
pose_change = (odometry[0], odometry[1] / np.pi * 180, odometry[2])
# Get current map bytes as grayscale
slam.getmap(mapbytes_)
# Update map
mapbytes[:] = mapbytes_
# Shut down the lidar connection
lidar.stop()
lidar.disconnect()
print('LIDAR::thread ends')