LiDAR Orbital
Debris Characterization
Detecting and characterizing space debris spin-rates using high-resolution point cloud analysis.
The Kessler Syndrome
With over 4,000 active satellites and countless fragments of defunct rockets in orbit, space debris poses a catastrophic risk to future missions. To capture or de-orbit this debris, we first need to know exactly how it is moving.
Capturing a tumbling object requires precise knowledge of its rotational velocity (RPM) and physical dimensions. The LASER project utilizes LiDAR to derive these metrics in real-time.
Hardware Architecture
The system integrates commercial off-the-shelf components to create a cost-effective sensing platform.
Intel RealSense L515 solid-state LiDAR camera capturing depth data at 30 FPS.
Raspberry Pi 5 (8GB) handles data acquisition, .bag recording, and initial PLY conversion.
Point Cloud Processing
Raw LiDAR data is noisy. I developed a Python pipeline using Open3D to isolate the target object and extract kinematic data.
def get_plane_normal(pcd):
# Segment the largest plane using RANSAC
plane_model, inliers = pcd.segment_plane(
distance_threshold=0.01,
ransac_n=3,
num_iterations=1000
)
# Extract normal vector [a, b, c]
[a, b, c, d] = plane_model
normal_vector = np.array([a, b, c])
# Normalize
return normal_vector / np.linalg.norm(normal_vector)By tracking the normal vector of the object's largest plane across timestamps, we calculate the angular velocity. Our tests achieved an RPM estimation accuracy of 99% against ground truth data.