LiDAR and Point Clouds

LiDAR (Light Detection and Ranging) is the "radar for robots." It shoots laser beams at the world and measures how long they take to bounce back. The result: direct distance measurements — no guessing, no calibration headaches, just raw 3D geometry.

How LiDAR Works

The core principle:

1. Emit a laser pulse toward a target
2. Wait for the reflection to return
3. Measure the time (usually nanoseconds)
4. Calculate distance using the speed of light:

distance = (speed_of_light * time) / 2

(Divided by 2 because the light travels to the target and back.)

Since light moves at ~300,000 km/s, even nanosecond precision gives millimeter-accurate measurements.

2D vs. 3D LiDAR

2D LiDAR (Laser Scanners)

• Spins in a single plane (usually horizontal)
• Outputs a 1D array of distances — one per angle
• Common range: 270° coverage, 0.25° angular resolution (~1080 points per scan)
• Used for: indoor navigation, obstacle avoidance, 2D mapping

# A single 2D scan
scan = {
  "angle_min": -2.356,       # radians (-135°)
  "angle_max": 2.356,        # radians (135°)
  "angle_increment": 0.0044, # radians (~0.25°)
  "ranges": [
    3.2,  # distance at -135°
    3.1,  # distance at -134.75°
    2.9,  # distance at -134.5°
    # ... 1080 total measurements
  ],
  "intensities": [
    50,   # reflection strength (0-255)
    48,
    52,
    # ... corresponding to each range
  ]
}

3D LiDAR (Spinning/Solid-State)

• Multiple laser beams in different vertical angles
• Outputs a 2D array or point cloud — (x, y, z) coordinates
• Common configurations: 16, 32, 64, or 128 beams
• Used for: 3D mapping, autonomous driving, aerial surveying

# A 3D point cloud
point_cloud = {
  "width": 1024,    # points per horizontal scan line
  "height": 64,     # number of vertical beams
  "points": [
    {"x": 2.5, "y": 0.3, "z": 0.1, "intensity": 120},
    {"x": 2.4, "y": 0.3, "z": 0.0, "intensity": 115},
    # ... (1024 * 64 = 65,536 points per scan)
  ]
}
 
# Total data per scan: ~1-2 MB (depending on encoding)
# At 10 Hz, that's 10-20 MB/sec

Point Clouds: The Data Structure

A point cloud is just a collection of 3D points. Each point has:

• Position: (x, y, z) in meters (or whatever unit)
• Intensity: How much laser light reflected (0-255 or 0-65535)
• Optional: color (if fused with camera), timestamp, beam ID

Organized vs. Unorganized

• Organized: Points form a grid (like image pixels) — fast to index by row/column
• Unorganized: Points in a flat list — more flexible but slower to query

Range and Intensity

Range (Distance)

• Maximum range: 10m (indoor LiDAR) to 200m+ (automotive LiDAR)
• Invalid readings: Returned as inf, NaN, or a special value (often 0 or max_range + 1)
• Failure cases: Transparent objects, very dark/absorbing surfaces, max range exceeded

Intensity (Reflectivity)

Measures how much laser light bounces back. High intensity means:

• Bright/reflective surfaces (white walls, metal, retroreflectors)
• Close objects (more photons return)

Low intensity means:

• Dark/absorbing surfaces (black rubber, asphalt)
• Far objects (signal weakens with distance)
• Glancing angles (laser hits at a steep angle, less reflection)

# Remove invalid points
valid_points = [p for p in points if 0.1 < p.z < 50.0]
 
# Remove low-confidence points (weak reflections)
confident_points = [p for p in valid_points if p.intensity > 30]
 
# Remove ground plane (assume z < 0.1 is floor)
non_ground = [p for p in confident_points if p.z > 0.1]
 
# Now you have clean data for obstacle detection

Common LiDAR Use Cases

What's Next?

LiDAR gives us 3D points, but cameras give us rich visual information. The next lesson explores depth perception — how we can get 3D distance data from cameras alone, and when to choose LiDAR vs. vision-based depth.