High Precision Indoor Navigation for Autonomous Vehicles
| Autor: | Eduardo Sánchez Morales, Michael Botsch, Bertold Huber, Andrés García Higuera |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Signal Processing (eess.SP) FOS: Computer and information sciences Mean squared error business.industry Orientation (computer vision) Computer science Real-time computing Automotive industry 02 engineering and technology 01 natural sciences Computer Science - Robotics Lidar Inertial measurement unit Position (vector) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Global Positioning System FOS: Electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Electrical Engineering and Systems Science - Signal Processing business Robotics (cs.RO) |
| Zdroj: | IPIN |
| Popis: | Autonomous driving is an important trend of the automotive industry. The continuous research towards this goal requires a precise reference vehicle state estimation under all circumstances in order to develop and test autonomous vehicle functions. However, even when lane-accurate positioning is expected from oncoming technologies, like the L5 GPS band, the question of accurate positioning in roofed areas, e.\,g., tunnels or park houses, still has to be addressed. In this paper, a novel procedure for a reference vehicle state estimation is presented. The procedure includes three main components. First, a robust standstill detection based purely on signals from an Inertial Measurement Unit. Second, a vehicle state estimation by means of statistical filtering. Third, a high accuracy LiDAR-based positioning method that delivers velocity, position and orientation correction data with a mean error of 0.1 m/s, 4.7 cm and 1$^\circ$ respectively. Runtime tests on a CPU indicates the possibility of real-time implementation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|