This page introduces ORBBEC®Dabai, a depth camera, and the RTAB-Map algorithm for mapping and navigation in robotics. It provides key parameters of the camera and offers detailed instructions for launching and utilizing it with the RTAB-Map algorithm. With clear steps and visual aids, users can efficiently integrate these technologies for real-time mapping and navigation in robotic applications.

Table of Contents

Introduction and use of ORBBEC®Dabai

ORBBEC®Dabai is a depth camera based on binocular structured light 3D imaging technology. It mainly includes a left infrared camera (IR camera1), a right infrared camera (IR camera2), an IR

projector, and a depth processor. The IR projector is used to project the structured light pattern (speckle pattern) to the goal scene, the left infrared camera and the right infrared camera

respectively collect the left infrared structured light image and the right infrared structured light

image of the goal, and the depth processor executes the depth calculation algorithm and outputs the depth image of the goal scene after receiving the left infrared structured light image and the right infrared structured light image.

| Parameter name | Parameter index | | --- | --- | | The distance between the imaging centers of the left and right infrared cameras |   40mm | | Depth distance | 0.3-3m | |     Power consumption | The average power consumption of the whole machine <2W; The peak value at the moment the laser is turned on <5W (duration: 3ms); Typical standby power consumption <0.7W. | |   Depth map resolution | 640X400@30FPS 320X200@30FPS | |   Color map resolution | 1920X1080@30FPS 1280X720@30FPS 640X480@30FPS | |   Accuracy | 6mm@1m (81% FOV area participates in accuracy calculation*) | | Depth FOV | H 67.9° V 45.3° | | Color FOV | H 71° V43.7° @1920X1080 | | Delay | 30-45ms | | Data transmission | USB2.0 or above | | Supported operating system | Android / Linux / Windows7/10 | | Power supply mode | USB | | Operating temperature | 10°C ~ 40°C | |   Applicable scene | Indoor / outdoor (specifically subject to application scenes and related algorithm requirements) | | Dustproof and waterproof | Basic dustproof | | Safety | Class1 laser | | Dimensions (mm) | Length 59.6 X width 17.4 X thickness 11.1mm |

After knowing the basic parameters of ORBBEC®Dabai, start to practice：

<aside> 📒 Note: Before running the command, please make sure that the programs in other terminals have been terminated. The termination command is: Ctrl+c

</aside>

  1. First ,start the ORBBEC®Dabai camera. Run the following command:

    ros2 launch astra_camera dabai.launch.py

  2. Open rqt_image_view:

    ros2 run rqt_image_view rqt_image_view

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Introduction of rtabmap algorithm

RTAB-Map (Real-Time Appearance-Based Mapping) is an algorithm for Simultaneous Localization and Mapping (SLAM) that aims to achieve a balance between real-time performance and map

quality. RTAB-Map is a graph-based SLAM system that can build dense 3D maps at runtime (real- time).

Some of the key features and components of RTAB-Map are as follows:

  1. Real-time Performance: RTAB-Map is specifically designed to operate in real-time applications, such as robot navigation and augmented reality systems. It employs an algorithm that minimizes computational requirements while achieving fast and accurate map construction and positioning, even with limited computing
  2. Feature-based SLAM: RTAB-Map utilizes visual and inertial sensor data to perform feature It extracts key points and descriptors from consecutive frames to enable simultaneous localization and mapping (SLAM), even without precise motion models. This feature allows for robust mapping and localization in dynamic environments.
  3. Environment Awareness: RTAB-Map incorporates environment awareness techniques to enhance map It takes into account depth information, parallax, and other environmental factors, which is particularly beneficial in scenarios with less texture or repetitive structures. This improves the reliability and accuracy of the generated maps.
  4. Loop Detection and Closed-loop Optimization: RTAB-Map includes loop detection mechanisms to identify previously visited areas within the It then employs optimization techniques to correct previous trajectories and maps based on the loop closure information. This ensures consistency in the map representation and reduces errors over time.
  5. RGB-D Sensor Support: RTAB-Map provides direct support for RGB-D sensors, such as the Microsoft By utilizing depth information from these sensors, RTAB-Map enhances the accuracy and density of the generated maps. This support for RGB-D sensors enables more detailed and comprehensive mapping capabilities.