One of the most popular research areas is low-cost navigation and positioning systems for autonomous vehicles. Determining a vehicle's position within a lane is critical for achieving high automation. Vehicle navigation and positioning relied heavily on the Global Navigation Satellite System (GNSS) service in open-sky scenarios. Nonetheless, GNSS signals were easily degraded due to various environmental situations such as urban canyons caused by multi-path effects and Non-Line-of-Sight (NLOS) issues. To perform robustly in complex scenarios, sensor fusion is the most common solution. The following paper presents a radar visual odometry framework to improve the lack of scale factors for monocular cameras and poor angular resolution for radar. The framework is based on the characteristics of camera and radar sensors which have complementary advantages in each other. The results show that the proposed framework can be used to estimate general 2D motion in an indoor environment and correct the unknown scale factor of Monocular Visual Odometry in a real-world setting.
|頁（從 - 到）||235-240|
|期刊||International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives|
|出版狀態||Published - 2022 5月 30|
|事件||2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission I - Nice, France|
持續時間: 2022 6月 6 → 2022 6月 11
All Science Journal Classification (ASJC) codes