Seabed Mapping IMU Calibration Method

Seabed Mapping IMU Calibration Method

Most of the ocean is unmapped, constraining scientific understanding across disciplines from geology to climate modelling.seabed mapping imu Addressing this gap is the motivation behind seabed mapping projects such as Seabed 2030.

While multibeam sonar systems enable seafloor mapping on ships and other marine vehicles, specialized robotic underwater vehicles are more efficient and less expensive for this work, allowing broader coverage and higher spatial resolution.seabed mapping imu A key challenge is to precisely integrate multiple sensors such as the multibeam echo sounder, GNSS receiver, IMU, and SVP into these vehicles in order to maintain accurate positioning.

In this article, we present an IMU calibration method based on the measurement of acoustic LBL, which can significantly reduce the position error of IMUs used in tightly coupled GNSS/LiDAR SLAM navigation systems.seabed mapping imu Our method uses the acoustic signal transmitted by the transducer as an external reference, reducing the required IMU internal sensor calibration to half of its conventional value. We validate the accuracy of our approach in a seabed mapping application using both acoustic and inertial measurements combined with the use of an independent GNSS receiver.

A high-resolution bathymetric map is essential for a variety of applications, including safe navigation and studying benthic habitats.seabed mapping imu However, it is remarkably difficult and costly to send vessels to remote locations to collect bathymetric data. Therefore, a variety of technologies have been developed to support the collection of high-resolution maps from a range of platforms.

These include multi-beam sonar systems that can be mounted on commercial ships and research vessels, as well as unmanned surface vehicles (USVs) for conducting hydrographic surveys and habitat mapping. USVs are also advantageous for mapping applications because they can be launched and recovered easily in shallow or hazardous waters.

In addition to the USV itself, a range of sensors can be integrated into an autonomous vehicle for seabed mapping, including an IMU and GNSS receiver for odometry and a LiDAR sensor for topographical mapping. GNSS and IMU sensors are generally implemented in the form of Microelectromechanical System (MEMS) or Fiber Optic Gyroscopes (FOG). Both offer advantages and disadvantages for specific underwater navigation tasks, but MEMS IMUs are often preferred for their compact size and low power consumption.

The position and velocity of the ASV are estimated from the GNSS/IMU data fusion, enabling high-accuracy and robust bathymetry and seabed mapping results. To achieve this, the acoustic and inertial sensors are time synchronized in order to maintain accuracy, minimize inaccuracies, and produce consistent sonar and LiDAR deliverables. Specifically, the acoustic sensor is used to determine a reference frame for the LiDAR and stereo-matched leading-edge linescans, while the IMU is used to compute trajectory estimates and odometry. Finally, the SONAR and LiDAR points are merged to generate a seabed-to-sky map that updates at a rate of 0.5 Hz. The position error of the ASV is determined by comparing it to this map, resulting in a seabed mapping solution that is accurate to 0.1 cm in a GNSS-denied coastal environment.

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