For SLAM, LiDAR data needs to be collected with timestamped distance, angle, and position information. The ESP32 can log this data to an SD card for later processing in tools like ROS (Robot Operating System), MATLAB, or Python-based SLAM algorithms.
Steps for SLAM Data Collection:
Read LiDAR Data
- Get distance and angle information.
- Convert LiDAR output into usable (x, y) coordinates.
Record Odometry (If Available)
- Track robot movement (e.g., encoder data, IMU data).
- Helps in localizing the LiDAR data.
Store Data Efficiently
- Save as CSV or JSON for easy processing in SLAM software.
Post-Process Data
- Use Python, MATLAB, or ROS to create the map.
- Apply ICP (Iterative Closest Point) or EKF (Extended Kalman Filter) algorithms.
code for logging Angle, distance and timestamp for SLAM:
#include <SD.h>
#include <SPI.h>
#include <HardwareSerial.h>
#define SD_CS 5 // Chip Select pin for SD card
HardwareSerial LiDARSerial(2); // UART2 for LiDAR
File dataFile;
void setup() {
Serial.begin(115200);
LiDARSerial.begin(115200, SERIAL_8N1, 16, 17);
if (!SD.begin(SD_CS)) {
Serial.println(“SD Card initialization failed!”);
return;
}
Serial.println(“SD Card Initialized.”);
dataFile = SD.open(“/slam_data.csv”, FILE_WRITE);
if (dataFile) {
dataFile.println(“Timestamp,Angle,Distance(mm)”); // CSV Headers
dataFile.close();
}
}
void loop() {
if (LiDARSerial.available()) {
uint8_t frame[9];
if (LiDARSerial.readBytes(frame, 9) == 9) {
if (frame[0] == 0x59 && frame[1] == 0x59) {
int distance = frame[2] | (frame[3] << 8);
int strength = frame[4] | (frame[5] << 8);
int angle = millis() % 360; // Simulated angle (Replace with encoder/IMU-based heading)
unsigned long timestamp = millis();
Serial.printf(“Time: %lu, Angle: %d, Distance: %d mmn”, timestamp, angle, distance);
dataFile = SD.open(“/slam_data.csv”, FILE_WRITE);
if (dataFile) {
dataFile.printf(“%lu,%d,%dn”, timestamp, angle, distance);
dataFile.close();
}
}
}
}
}
Visualizing SLAM Data in Python:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load the data
data = pd.read_csv(“slam_data.csv”)
# Convert polar to Cartesian coordinates
angles = np.radians(data[“Angle”])
x = data[“Distance(mm)”] * np.cos(angles)
y = data[“Distance(mm)”] * np.sin(angles)
# Plot the LiDAR points
plt.figure(figsize=(8,8))
plt.scatter(x, y, s=5, color=’red’)
plt.xlim(-5000, 5000)
plt.ylim(-5000, 5000)
plt.xlabel(“X (mm)”)
plt.ylabel(“Y (mm)”)
plt.title(“LiDAR Data for SLAM”)
plt.grid(True)
plt.show()
Summary:
- The ESP32 logs LiDAR angle & distance to an SD card.
- Data is formatted for SLAM processing in Python/ROS.
- A Python script converts data into a 2D map.
- Future work: IMU, Encoders, Real-time SLAM integration.