Understanding and Working with LDRobot LiDAR Sensors: A Developer's Guide

LiDAR (Light Detection and Ranging) technology has become increasingly accessible to makers, robotics enthusiasts, and developers. In this blog post, I’ll walk through how to interface with LDRobot’s LiDAR sensors using Python, breaking down the code and explaining the underlying concepts of LiDAR data processing.

TLDR; LDRobot Lidar Parser

Understanding the Data Protocol

Please note that this code has been tested only on the STL19P model, but should work for all with minor tweak in the parameters. Before getting into the code, let’s take a moment to understand the data frame structure used by LDRobot. The protocol is documented in the LDRobot GitHub repository .

Each data packet from the LiDAR follows a specific structure:

• 49 bytes per packet
• 12 data points per packet
• Uses CRC8 for error checking
• Contains information about rotation speed, angles, and distance measurements

The Code Structure

Let’s break down the two Python files:

1. crc_utils.py: Contains a lookup table for CRC8 calculation
2. lidar.py: Main implementation for interacting with the LiDAR sensor

CRC8 Error Checking

Error checking is crucial when working with serial data. The crc_utils.py file contains a lookup table for performing CRC8 (Cyclic Redundancy Check) calculations:

crc_table = [
    0x00, 0x4D, 0x9A, 0xD7, 0x79, 0x34, 0xE3, 0xAE,
    # ... table continues
]

This table provides a fast way to calculate checksums for data validation.

The LIDAR Class

Now, let’s examine the main lidar.py file, which contains the LIDAR class for interfacing with the sensor:

class LIDAR:
    def __init__(self, serial_port, baudrate):
        self.PACKET_LENGTH = 49
        self.POINT_PER_PACK = 12
        self.serial_conn = serial.Serial(serial_port, baudrate=baudrate, timeout=1)

The initialization sets up constants and establishes a serial connection with the LiDAR sensor. The key parameters are:

• serial_port: The port where the LiDAR is connected (e.g., /dev/ttyUSB0 on Linux)
• baudrate: Communication speed (230400 bps for this model)

Step 1: Calculating CRC8 Checksums

The calculate_crc8 method uses the lookup table to verify data integrity:

def calculate_crc8(self, data):
    crc = 0x00
    for byte in data:
        crc = crc_table[(crc ^ byte) & 0xFF]
    return crc

This function:

1. Starts with an initial value of 0x00
2. For each byte in the data, performs an XOR operation with the current CRC value
3. Uses the result as an index into the CRC table to get the next CRC value
4. Returns the final CRC value after processing all bytes

Step 2: Parsing LiDAR Packets

The most critical method is parse_packet, which extracts meaningful information from the raw data:

def parse_packet(self, packet):
    if len(packet) != self.PACKET_LENGTH:
        print("Invalid packet length.")
        return
    if packet[0] != 0x54 or packet[1] != 0x2C:
        print("Invalid packet header")
        return
    received_crc = packet[self.PACKET_LENGTH - 3]
    calculated_crc = self.calculate_crc8(packet[: self.PACKET_LENGTH - 3])
    if received_crc != calculated_crc:
        print("CRC8 checksum mismatch")
        return
    # ... continue parsing

This portion:

1. Verifies the packet length
2. Checks for the correct header (0x54, 0x2C)
3. Validates the CRC8 checksum

Once validated, the function extracts:

header, ver_len, speed, start_angle = struct.unpack("<BBHH", packet[:6])

• header: The packet header (should be 0x54)
• ver_len: Version and length information
• speed: Motor rotation speed in RPM
• start_angle: Starting angle of the scan in this packet (in hundredths of a degree)

Step 3: Processing Distance Data

For each of the 12 points in the packet:

points = []
offset = 6  # 4 + 2 bytes
for _ in range(self.POINT_PER_PACK):
    distance, intensity = struct.unpack(
        "<HB", packet[offset : offset + 3]
    )  # 3-> 2+1 for slicing
    points.append({"distance": distance, "intensity": intensity})
    offset += 3

For each point, we extract:

• distance: Distance measurement in millimeters (16-bit unsigned integer)
• intensity: Reflection intensity value (8-bit unsigned integer)

Step 4: Handling Angular Information

The end angle and timestamp are extracted:

end_angle, timestamp = struct.unpack("<HH", packet[42:46])

Since LiDAR scans are rotational, we need to calculate the angle for each point:

# convert angles from hundredth of a degree to degrees
start_angle = (start_angle % 36000) / 100.0
end_angle = (end_angle % 36000) / 100.0
# calculate angle increment
angle_diff = (end_angle - start_angle + 360.0) % 360.0
angle_increment = angle_diff / 11  # 12 points, so 11 intervals
# interpolate angles for each point
angles = [
    (start_angle + i * angle_increment) % 360.0
    for i in range(self.POINT_PER_PACK)
]

This code:

1. Converts raw angle values (in hundredths of a degree) to actual degrees
2. Calculates the difference between start and end angles
3. Determines the angle increment between consecutive points
4. Interpolates angles for each measured point

Step 5: Reading LiDAR Data

The read_lidar_data method handles the continuous process of reading packets:

def read_lidar_data(self):
    # read until header and ver_len values are found
    packet = self.serial_conn.read_until(b"\x54\x2C")
    if len(packet) != (self.PACKET_LENGTH - 2):
        return
    packet = bytes(b"\x54\x2C") + packet  # needed for checksum verification
    if len(packet) == self.PACKET_LENGTH:
        if data := self.parse_packet(packet):
            return data

This function:

1. Reads from the serial port until the packet header (0x54, 0x2C) is found
2. Ensures the packet is complete
3. Reconstructs the full packet for parsing
4. Calls parse_packet to extract the data

Step 6: Putting It All Together

The main execution block shows how to use the class:

if __name__ == "__main__":
    lidar = LIDAR(serial_port="/dev/ttyUSB0", baudrate=230400)
    try:
        while True:
            data = lidar.read_lidar_data()
            if data:
                print(f"Speed: {data['speed']} RPM")
                print(f"Start Angle: {data['start_angle']:.2f}°")
                print(f"End Angle: {data['end_angle']:.2f}°")
                print(f"Timestamp: {data['timestamp']}")
                for point in data["scan_data"]:
                    print(
                        f"Angle: {point['angle']:.2f}°, Distance: {point['distance']} mm, Intensity: {point['intensity']}"
                    )
                print("\n---\n")
    except KeyboardInterrupt:
        print("\nStopping Lidar data reading.")
    finally:
        lidar.close_serial_connection()

This code:

1. Creates a LIDAR object with the appropriate port and baudrate
2. Continuously reads and processes data packets
3. Prints human-readable information about each scan
4. Properly closes the connection when interrupted

Improving the Code

Here are some potential improvements to consider:

1. Multithreading: Separate data collection from processing for better performance
2. Data Visualization: Add real-time plotting of the LiDAR data
3. Filtering: Implement noise reduction and outlier removal
4. SLAM Implementation: Add Simultaneous Localization and Mapping algorithms
5. ROS Integration: Package the code as a ROS node for robotics applications via Python API as module in C++ is already available.

Conclusion

Working with LiDAR sensors provides a powerful way to give your robots or projects “vision” of their surroundings. The code we’ve analyzed provides a solid foundation for interfacing with LDRobot LiDAR sensors, handling the complex binary protocol, and extracting meaningful spatial data.

By understanding each step of the process—from CRC checking to angle interpolation—you now have the knowledge to adapt this code to your specific needs or troubleshoot any issues that might arise when working with these sensors.

Whether you’re building an autonomous robot, creating a security system, or just experimenting with spatial sensing, this LiDAR interface opens up exciting possibilities for your projects.