LoopyLuci SLAM — Simultaneous Localization and Mapping
Implementing SLAM for robotics — from Lidar SLAM (GMapping, Cartographer) through Visual SLAM (ORB-SLAM), loop closure, and sensor fusion.
When to Use
- Building robot that navigates unknown environments
- Generating maps from sensor data for autonomous navigation
- Localizing robot within existing map
- Visual-inertial odometry for AR/VR
- Autonomous vehicle localization
SLAM Approaches
SLAM_APPROACHES = {
'lidar_slam': 'GMapping, Cartographer, Karto — 2D/3D lidar, grid maps, loop closure',
'visual_slam': 'ORB-SLAM3, DSO, SVO — camera-only, feature-based or direct',
'visual_inertial': 'VINS-Mono, OKVIS — camera + IMU fusion, robust to rapid motion',
'multi_sensor': 'Lidar + camera + IMU + GPS — sensor fusion for robust SLAM',
}
class SLAMPipeline:
"""SLAM pipeline components."""
STATE_ESTIMATION = ['Odometry', 'Scan matching (ICP)', 'Graph optimization', 'Loop closure detection']
@staticmethod
def evaluate_slam(estimated_path: np.array, ground_truth: np.array) -> Dict:
from evo.core import metrics
ape = metrics.APE(metrics.PosePath3D(estimated_path), metrics.PosePath3D(ground_truth))
return {
'rmse': round(ape.RMSE, 4),
'mean': round(ape.mean, 4),
'std': round(ape.std, 4),
}
Verification Checklist
- SLAM approach chosen (lidar, visual, visual-inertial)
- Sensor calibration performed (camera intrinsics, IMU biases, extrinsics)
- Loop closure detection working (recognizing revisited places)
- Map quality evaluated (consistency, drift over distance)
- Real-time performance (processing time < sensor frame rate)
- Localization accuracy measured (ATE, RPE metrics)
- Degenerate cases handled (featureless environments, rapid motion)