My time-of-flight proximity sensors of my Raven (formerly Puck) robot use the VL53L0X chips and are fairly noisy. I wanted to have a quick way to visualize just how noisy they were. Here is the graph I produced:
The two rows represent the front versus back of the robot frame. The four columns represent the two pairs of sensors for the left and right sides of the robot frame. At each corner of the robot frame, there is a time-of-flight sensor that is pointed outward to the side (left or right) and another that is pointed either forward or backward. Each histogram is showing normalized counts per distance over the last sampling period.
As you can see from the histograms, some of the sensors are fairly noisy. This is with the robot not moving and nothing nearby is moving. Readings at 0 mm should be ignored as I moved all readings that were beyond 2 meters into the 0 meter bucket as they are uninteresting. The histogram in the third column of the first row shows a fairly wide variance in readings, while the histogram in the last column of the last row show a pretty narrow variance.
Below is the python code used to generate the plots. It includes code to read the ROS 2 messages generated by my custom monitor computer and uses matplotlib to do the display.
from datetime import datetime
import rclpy
from rclpy.node import Node
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
from sensor_msgs.msg._range import Range
from std_msgs.msg import String
class MinimalSubscriber(Node):
def __init__(self):
super().__init__('minimal_subscriber')
self.start_time = datetime.now()
self.callback_count = 0
# These are the title strings shown per sensor histogram.
# They are a shortcut, for me, as to the physical position of the corresponding sensor.
# The 'X" shows where the sensor is located.
# The '<' and '>' show whether the sensor is on the left or right side of the robot frame.
# The '^' and 'v' show whether the sensor is on the front or rear of the robot frame.
self.sensor_names = [['X<^-', '-<^X', 'X^>-', '-^>X'],
['X<v-', '-<vX', 'Xv>-', '-v>X']]
self.number_sensors = 8 # There are 8 sensors.
self.number_values_to_cache = 20 # I want to show the variance over this number of the last readings.
self.last_n_values_per_sensor = np.zeros(
(self.number_sensors, self.number_values_to_cache), dtype='float')
self.next_index_number = np.zeros((self.number_sensors), dtype='int32')
# Create an array of histograms.
# Two rows for front vs back of robot.
# Four columns for left-sideways, left-front-or-back, right-front-or-back, right-sideways position.
self.figure, self.axis = plt.subplots(
nrows=2, ncols=4, sharex=False, sharey=False, squeeze=False, figsize=(8, 2))
# Set the window title.
self.figure.canvas.set_window_title('Time Of Flight Sensors step')
# Create the x-axis values. I'm interested in only ranges from 0.00 to 1.00 meters.
self.bins = [x / 100.0 for x in range(100)]
# Make it all look pretty.
plt.subplots_adjust(hspace=0.6)
plt.autoscale(enable=True, axis='both', tight=True)
plt.rcParams['lines.linewidth'] = 1
# Set up the ROS 2 quality of service in order to read the sensor data.
qos_profile = QoSProfile(
reliability=QoSReliabilityPolicy.BEST_EFFORT,
history=QoSHistoryPolicy.KEEP_LAST,
depth=1
)
# Subscribe to the sensor topics.
for sensor_number in range(8):
self.subscription = self.create_subscription(
Range,
'/tof{s}Sensor'.format(s = sensor_number),
self.listener_callback,
qos_profile,
)
# Set up the 8 histogram formats and titles.
self.patches = [1, 2, 3, 4, 5, 6, 7, 8]
for row in range(2):
for col in range(4):
n, bins, patches = self.axis[row][col].hist(
self.last_n_values_per_sensor[row][col], self.bins, histtype='bar')
self.patches[(row * 4) + col] = patches
self.axis[row, col].set_title(
self.sensor_names[row][col], fontsize=8, fontfamily='monospace')
# Let's go.
plt.ion()
plt.show()
self.subscription # prevent unused variable warning
# Process a time-of-flight sensor message of type Range.
def listener_callback(self, msg):
self.callback_count = self.callback_count + 1
sensor_number = int(msg.header.frame_id[-1]) # Get the sensor number.
range_value = msg.range
if (range_value > 2.0):
# If the range is greater than 2 meters, ignore it by setting it to zero.
range_value = 0
# Capture the last readings of the sensor in a ring buffer.
self.last_n_values_per_sensor[sensor_number][self.next_index_number[sensor_number]] = range_value
if (self.callback_count % 24) == 0:
# Peridically update the plots.
for s in range(8):
# For each sensor, create a histogram.
data = self.last_n_values_per_sensor[s]
n, _ = np.histogram(data, self.bins, density=True)
max = n.max()
for count, rect in zip(n, self.patches[s]):
rect.set_height(count / max) # Normalize the height of the rectangle.
self.figure.canvas.draw()
self.figure.canvas.flush_events()
# Print out the frames per second of sensor data for all 8 sensors since the last plot update.
# Divide by 8 if you want to know the frames per second per sensor.
duration = datetime.now() - self.start_time
fps = self.callback_count / (duration.seconds + (duration.microseconds / 1000000.0))
print("callback_count: %d, duration: %f, fps: %3.2f" % (self.callback_count, (duration.seconds + (duration.microseconds / 1000000.0)), fps))
# Update the ring buffer index.
self.next_index_number[sensor_number] = self.next_index_number[sensor_number] + 1
if self.next_index_number[sensor_number] >= self.number_values_to_cache:
self.next_index_number[sensor_number] = 0
def main(args=None):
rclpy.init(args=args)
minimal_subscriber = MinimalSubscriber()
rclpy.spin(minimal_subscriber)
minimal_subscriber.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()