Sensor Setup
- 1 minSensor Setup
We argue that while smooth motion, clear images, and dense point clouds obviously facilitate algorithm processing, they may not be sufficient for practical applications. Therefore, we select cost-effective and widely used sensors to replicate real-world robotic application conditions as closely as possible.
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LiDAR: We utilize the Velodyne VLP-16 LiDAR to capture 3D point clouds of the surrounding environment at 10 Hz, collecting over 300,000 points per second. The sensor features a 360° horizontal field of view (FoV) and a 30° vertical FoV, with a scanning range over 100 m. To ensure efficient data transmission, we connect the LiDAR to the host via an Ethernet port.
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Camera: The Intel RealSense D455 is a global shutter RGB-D camera capable of capturing time-synchronized color and depth images at a resolution of 720×480 pixels at 10 Hz, whose optimal depth range extends from 0.6 m to 6 m from the image plane, with an error margin of less than 2% within a 4 m range.
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IMU: To ensure higher accuracy in IMU data acquisition, we select not to use the internal IMU (Bosch BMI055) of the RealSense D455. Instead, we integrate the Xsens MTi 300 to capture 9-axis measurement data at 200 Hz. The accelerometer exhibits a bias stability of 15 \(\mu g\) and a noise density of 60 \(\mu g/\sqrt{Hz}\), the gyroscope achieves a bias stability of 10 \(°/s\) with a noise density of 0.003 \(°/s\), and the magnetometer provides a resolution of 0.25 \(mG\).
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GNSS: The M68UGI-G GNSS receiver is utilized to acquire raw positioning data, including GPS satellite count, timestamps, signal quality, and other relevant information at 5 Hz. By leveraging RTK technology, high-precision robot positions are estimated as ground truth for the trajectory, achieving a horizontal accuracy of \(\pm(8mm+1ppm)\) and a vertical accuracy of \(\pm(15mm+1ppm)\).