---
id: wiki-2026-0508-automated-mapping
title: Automated Mapping (SLAM / HD Map)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [자동 매핑, SLAM, HD map, point cloud, bundle adjustment, loop closure, 3D reconstruction, NeRF]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [slam, hd-map, lidar, point-cloud, bundle-adjustment, loop-closure, robotics, autonomous-vehicles, nerf, 3d-reconstruction]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: C++ / Python
  framework: ROS / Open3D / COLMAP / OpenVSLAM
---

# Automated Mapping

## 📌 한 줄 통찰
> **"매 unknown 의 explore + 매 self-localize 의 simultaneous"**. 매 SLAM (Simultaneous Localization and Mapping). 매 sensor (LiDAR, camera, IMU) 의 fusion. 매 robotics / AV / AR / VR 의 spatial intelligence 의 base. 매 modern: 매 NeRF / Gaussian Splatting 의 photoreal map.

## 📖 핵심

### 매 SLAM 의 4 stage
1. **Sensor data**: LiDAR / camera / IMU / GPS.
2. **Feature extraction**: ORB, SIFT, SuperPoint, LoFTR.
3. **Pose + map estimation**: 매 EKF / particle filter / graph.
4. **Loop closure + global optimization**: 매 bundle adjustment.

### 매 SLAM type

#### Visual SLAM
- 매 camera only.
- 매 ORB-SLAM3 (state-of-the-art classic).
- 매 DROID-SLAM (deep learning).

#### LiDAR SLAM
- 매 point cloud.
- 매 LOAM, LeGO-LOAM, FAST-LIO.
- 매 sparse + accurate.

#### Visual-Inertial (VIO)
- 매 camera + IMU.
- 매 VINS-Fusion, OpenVINS.
- 매 robotics, AR/VR.

#### LiDAR-Visual-Inertial
- 매 multi-sensor fusion.
- 매 LIO-SAM, FAST-LIVO.

### 매 핵심 component

#### Front-end
- 매 feature extraction.
- 매 matching (RANSAC).
- 매 motion estimation.

#### Back-end
- 매 graph optimization.
- 매 g2o, Ceres, GTSAM.
- 매 nonlinear least squares.

#### Loop closure
- 매 same place revisit 의 detect.
- 매 DBoW2, NetVLAD.
- 매 drift 의 correct.

#### Mapping
- 매 occupancy grid (2D).
- 매 OctoMap (3D voxel).
- 매 mesh / point cloud.

### Bundle Adjustment (BA)
- 매 nonlinear optimization.
- 매 reprojection error 의 minimize.
- 매 camera pose + 3D point 의 동시 추정.
- 매 SLAM 의 backbone.

### Modern / deep learning
- **DROID-SLAM**: 매 differentiable.
- **NeRF** (Neural Radiance Field): 매 photorealistic 3D.
- **Gaussian Splatting** (3DGS, 2023): 매 fast NeRF alternative.
- **NICE-SLAM**: 매 dense neural SLAM.
- **Gaussian-SLAM**.

### HD Map (autonomous driving)
- 매 lane geometry.
- 매 traffic sign / signal.
- 매 routing graph.
- 매 cm-level accuracy.
- 매 update mechanism.

### 매 응용
1. **Autonomous vehicle**: HD map.
2. **Drone**: indoor + outdoor.
3. **AR / VR**: room understanding (ARKit, ARCore).
4. **Robot vacuum**: 매 home map.
5. **Indoor robot**: 매 warehouse, 매 hospital.
6. **Surveying**: 매 building, 매 mine.
7. **Underwater**: 매 sonar + visual.
8. **Photogrammetry**: 매 cultural heritage.

### 매 challenge
1. **Dynamic objects**: 매 person, vehicle.
2. **Featureless environment**: 매 white wall.
3. **Lighting**: 매 dark / bright extremes.
4. **Long-term map**: 매 changing environment.
5. **Scale ambiguity** (monocular): 매 metric scale.
6. **Computational cost**: 매 real-time.

## 💻 패턴

### ORB-SLAM3 (C++)
```bash
# 매 build
mkdir build && cd build && cmake .. && make -j8

# 매 run with EuRoC dataset (visual-inertial)
./Examples/Stereo-Inertial/stereo_inertial_euroc \
    Vocabulary/ORBvoc.txt \
    Examples/Stereo-Inertial/EuRoC.yaml \
    /path/to/V1_01_easy \
    Examples/Stereo-Inertial/EuRoC_TimeStamps/V101.txt
```

### Python visual SLAM (pyslam-style)
```python
import cv2
import numpy as np

class SimpleVO:
    def __init__(self, K):
        self.K = K  # 매 camera intrinsic
        self.orb = cv2.ORB_create(2000)
        self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
        self.prev_kp, self.prev_des = None, None
        self.pose = np.eye(4)
    
    def process(self, frame):
        kp, des = self.orb.detectAndCompute(frame, None)
        if self.prev_des is None:
            self.prev_kp, self.prev_des = kp, des
            return self.pose
        
        matches = self.matcher.match(self.prev_des, des)
        matches = sorted(matches, key=lambda x: x.distance)[:200]
        
        pts1 = np.array([self.prev_kp[m.queryIdx].pt for m in matches])
        pts2 = np.array([kp[m.trainIdx].pt for m in matches])
        
        E, mask = cv2.findEssentialMat(pts1, pts2, self.K, cv2.RANSAC, 0.999, 1.0)
        _, R, t, _ = cv2.recoverPose(E, pts1, pts2, self.K, mask=mask)
        
        T = np.eye(4)
        T[:3, :3] = R
        T[:3, 3:] = t
        self.pose = self.pose @ T
        
        self.prev_kp, self.prev_des = kp, des
        return self.pose
```

### Open3D (point cloud)
```python
import open3d as o3d

# 매 load + visualize
pcd = o3d.io.read_point_cloud('scan.ply')
o3d.visualization.draw_geometries([pcd])

# 매 ICP registration
source = o3d.io.read_point_cloud('scan1.ply')
target = o3d.io.read_point_cloud('scan2.ply')

result = o3d.pipelines.registration.registration_icp(
    source, target,
    max_correspondence_distance=0.5,
    estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(),
)
print(result.transformation)
```

### COLMAP (photogrammetry)
```bash
# 매 image set → 매 3D reconstruction
colmap automatic_reconstructor \
    --workspace_path /path/to/workspace \
    --image_path /path/to/images
```

### NeRF (instant-NGP)
```python
import tinycudann as tcnn
import torch

# 매 hash grid encoding (instant-NGP)
encoder = tcnn.Encoding(n_input_dims=3, encoding_config={
    'otype': 'HashGrid',
    'n_levels': 16,
    'n_features_per_level': 2,
    'log2_hashmap_size': 19,
    'base_resolution': 16,
    'per_level_scale': 1.5,
})
mlp = tcnn.Network(n_input_dims=encoder.n_output_dims, n_output_dims=4, network_config={
    'otype': 'FullyFusedMLP', 'activation': 'ReLU',
    'output_activation': 'None', 'n_neurons': 64, 'n_hidden_layers': 2,
})

def render(rays_o, rays_d):
    samples = sample_along_rays(rays_o, rays_d)
    encoded = encoder(samples)
    rgb_sigma = mlp(encoded)
    return volume_render(rgb_sigma, samples)
```

### Gaussian Splatting (3DGS, 2023)
```bash
# 매 SfM 의 result 의 import
python train.py -s /path/to/colmap-output -m /path/to/output

# 매 view interactive
./SIBR_remoteGaussian_app -m /path/to/output
```

### Loop closure (DBoW3)
```cpp
#include <DBoW3/DBoW3.h>

DBoW3::Vocabulary vocab("ORBvoc.bin");
DBoW3::Database db(vocab, false, 0);

// 매 keyframe 마다 add
DBoW3::BowVector bow;
vocab.transform(descriptors, bow);
db.add(bow);

// 매 query: 매 매 frame 의 lookup
DBoW3::QueryResults ret;
db.query(bow, ret, 5);
if (ret[0].Score > 0.7) {
    // 매 loop closure detected!
}
```

## 🤔 결정 기준
| 상황 | Approach |
|---|---|
| Indoor robot | Visual-Inertial (ORB-SLAM3) |
| Outdoor AV | LiDAR + camera + IMU + GPS |
| AR (mobile) | ARKit / ARCore |
| Photoreal 3D | Gaussian Splatting |
| Photogrammetry | COLMAP |
| Drone outdoor | VIO + GPS |
| Robot vacuum | LiDAR 2D SLAM |
| Photoreal AR | NeRF / 3DGS |

**기본값**: Visual SLAM = ORB-SLAM3. LiDAR = LIO-SAM. Photoreal = Gaussian Splatting.

## 🔗 Graph
- 부모: [[Robotics]] · [[Computer Vision|Computer-Vision]] · [[Spatial-Computing]]
- 응용: [[Autonomous-Vehicles]] · [[HD-Map]]
- Modern: [[NeRF]] · [[Gaussian-Splatting]]
- Adjacent: [[Bundle-Adjustment]] · [[Loop-Closure]] · [[Bayesian-Brain-Hypothesis]]

## 🤖 LLM 활용
**언제**: 매 robot navigation. 매 AR/VR system. 매 3D reconstruction. 매 AV mapping.
**언제 X**: 매 2D image processing only. 매 single static image (use SfM).

## ❌ 안티패턴
- **Pure visual outdoor (no IMU)**: 매 fast motion 의 lose.
- **No loop closure**: 매 drift 폭발.
- **Static map assumption** (urban): 매 dynamic obj 의 noise.
- **Featureless environment**: 매 SLAM fail (LiDAR 의 fall back).
- **Offline only**: 매 real-time latency 의 ignore.
- **No relocalization**: 매 lost 시 의 recovery X.

## 🧪 검증 / 중복
- Verified (ORB-SLAM3, FAST-LIO, NeRF, 3DGS papers).
- 신뢰도 A.
- Related: [[Autonomous-Vehicles]] · [[Computer Vision|Computer-Vision]] · [[Robotics]] · [[NeRF]] · [[Gaussian-Splatting]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — SLAM type + ORB-SLAM3 + Open3D + NeRF + 3DGS code |
