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Improve lidar documentation structure and add point cloud aggregation functionality

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xiexun
2025-09-24 14:13:07 +08:00
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Lidar.MD
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# OrbbecSDK ROS2 LiDAR Driver
This ROS2 driver supports your use of Orbbec single-line/multi-line LiDAR. This document provides installation instructions, usage guides, and other important information to help you quickly get started using this driver.
This ROS2 driver supports your use of Orbbec single-line/multi-line LiDAR. This document provides installation instructions## 4. IMU Data
### 4.1 IMU Topics
When IMU is enabled, the following topics will be published:
- **`/lidar/imu/sample`**: Unified IMU topic containing synchronized accelerometer and gyroscope data in `sensor_msgs/Imu` format.
- **`/lidar/lidar_to_imu`**: Transform from LiDAR frame to IMU frame.
### 4.2 Using IMU Dataides, and other important information to help you quickly get started using this driver.
## 1. Installation
@@ -30,15 +39,15 @@ sudo bash install_udev_rules.sh
sudo udevadm control --reload-rules && sudo udevadm trigger
```
### 2. Getting Started
### 1.4 Build the Package
```bash
cd ~/ros2_ws/
# build release, Default is Debug
# Build release version, default is Debug
colcon build --event-handlers console_direct+ --cmake-args -DCMAKE_BUILD_TYPE=Release
```
Launch the LiDAR node
### 1.5 Launch the LiDAR Node
* First terminal
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- **imu_rate**: Unified frequency of the IMU (both accelerometer and gyroscope).
- **accel_range**: Range of the accelerometer.
- **gyro_range**: Range of the gyroscope.
- **linear_accel_cov**: Linear acceleration covariance value, default is `0.0001`.
- **angular_vel_cov**: Angular velocity covariance value, default is `0.0001`.
## Point Cloud Data Detailed Description
## 3. Point Cloud Data Details
### 3.1 Point Cloud Format
PointCloud2 (PointXYZITO) point cloud format is as follows:
```
float32 x # X axis, unit:m
float32 y # Y axis, unit:m
float32 z # Z axis, unit:m
uint8 intensity # lidar intensity
uint8 tag # lidar tag
uint32 offset_time # Point cloud offset time relative to topic time, in nanoseconds
float32 x # X axis, unit: meters
float32 y # Y axis, unit: meters
float32 z # Z axis, unit: meters
uint8 intensity # LiDAR intensity
uint8 tag # LiDAR tag
uint32 offset_time # Point cloud offset relative to topic time, unit nanoseconds
```
## 3. IMU Data
### 3.2 Point Cloud Aggregation Functionality
### 3.1 IMU Topics
The `publish_n_pkts` parameter enables point cloud aggregation functionality, which allows the LiDAR to accumulate a specified number of frames before publishing, then merge these frames into a larger point cloud data package for publishing.
#### Features:
- **Parameter Range**: 1-12000 frames
- **Applicable Formats**: Only effective when lidar format is `LIDAR_POINT` or `LIDAR_SPHERE_POINT`
- **Default Value**: 1 (no aggregation, each frame published individually)
- **Purpose**: Improve point cloud density, suitable for applications requiring denser point cloud data
#### Usage Examples:
```bash
# Aggregate 10 frames before publishing
ros2 launch orbbec_camera lidar.launch.py lidar_format:=LIDAR_POINT publish_n_pkts:=10
# Aggregate 100 frames before publishing
ros2 launch orbbec_camera lidar.launch.py lidar_format:=LIDAR_SPHERE_POINT publish_n_pkts:=100
```
**Note**: Increasing the `publish_n_pkts` value will improve point cloud density but will also increase latency and memory usage. Please adjust according to actual application requirements.
## 4. IMU Data
### 4.1 IMU Topics
When IMU is enabled, the following topics will be published:
- **`/lidar/imu/sample`**: Unified IMU topic containing synchronized accelerometer and gyroscope data in `sensor_msgs/Imu` format.
- **`/lidar/lidar_to_imu`**: Transform from LiDAR frame to IMU frame.
### 3.2 Using IMU Data
### 4.2 Using IMU Data
To enable IMU data collection: