Georeferencing of Laser Scanner-Based Kinematic Multi-Sensor Systems in the Context of Iterated Extended Kalman...

Area of Science:

• Robotics and Sensor Fusion
• Geomatics Engineering
• State Estimation

Background:

• Accurate georeferencing is vital for kinematic multi-sensor systems (MSS) in diverse environments.
• Global navigation satellite systems (GNSS) and total stations are often unreliable or inapplicable indoors or in challenging terrains.
• Prior information, such as geometrical constraints, significantly aids pose estimation in these scenarios.

Purpose of the Study:

• To introduce a general georeferencing approach for kinematic MSS using recursive state estimation.
• To develop a flexible framework capable of handling various observation inputs and (non)linear systems.
• To evaluate the impact of state constraints and inertial measurement unit (IMU) dependencies on georeferencing accuracy.

Main Methods:

• Utilized an iterated extended Kalman filter for recursive state estimation.
• Developed a general georeferencing framework accommodating diverse observation inputs and (non)linear models.
• Incorporated explicit/implicit formulations and (non)linear equality/inequality state constraints.
• Evaluated the system using an indoor kinematic MSS with a terrestrial laser scanner and simulated IMU data.

Main Results:

• Demonstrated the effectiveness of the recursive state estimation approach for georeferencing kinematic MSS.
• Quantified the impact of different state constraint combinations on system performance.
• Analyzed the influence of two classes of inertial measurement units (IMU) on georeferencing accuracy.
• Validated the framework using real-world indoor kinematic data and simulated IMU measurements.

Conclusions:

• The proposed georeferencing framework offers a robust solution for kinematic MSS in challenging environments.
• The integration of state constraints and appropriate IMU selection significantly improves pose estimation accuracy.
• The recursive state estimation method provides a versatile and effective approach for multi-sensor system georeferencing.