# cartographer/sensor/compressed_point_cloud.h

## Overview

`CompressedPointCloud` provides lossy compression for point clouds using grid-based quantization. Reduces memory/storage for large 3D lidar scans.

## Class: `cartographer::sensor::CompressedPointCloud`

### Compression Algorithm

1. **Quantize** points to a 3D grid (configurable resolution)
2. **Deduplicate** points in same voxel
3. **Encode** as integer grid indices + residuals
4. **Store** as byte arrays

### Construction

```cpp
explicit CompressedPointCloud(const PointCloud& point_cloud,
                              float compression_ratio = 0.1f);
```

- `compression_ratio` — Target size ratio (0.1 = 10% of original)

### Decompression

```cpp
PointCloud Decompress() const;
```

Returns approximate original point cloud.

### Serialization

```cpp
proto::CompressedPointCloud ToProto() const;
static CompressedPointCloud FromProto(const proto::CompressedPointCloud& proto);
```

### Proto Format

```proto
message CompressedPointCloud {
  int32 version = 1;
  float resolution = 2;
  repeated int32 cell_indices = 3;   // Packed 3D indices
  repeated float residuals = 4;      // Quantization residuals
}
```

## Usage

```cpp
// Compress large point cloud
sensor::PointCloud large_cloud = lidar->GetScan();
sensor::CompressedPointCloud compressed(large_cloud, 0.1f);

// Store/transmit
auto proto = compressed.ToProto();
SaveToFile(proto);

// Decompress for processing
sensor::PointCloud decompressed = compressed.Decompress();
```

## Configuration

Compression ratio tradeoffs:
- `0.05` — High compression, more loss
- `0.1` — Balanced (default)
- `0.2` — Lower compression, higher fidelity

## See Also

- [PointCloud](point_cloud.md) — Uncompressed representation
- [RangeData](range_data.md) — SLAM input format