一个视觉/惯性导航/全球导航卫星综合系统,用于陆地车辆的相对和绝对定位
Yao Zhang1, Liang Chu1, Yabin Mao1
1National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究通过将视觉惯性测距 (VIO) 与全球导航卫星系统 (GNSS) 数据融合,提高了自动驾驶车辆的定位. 适应性机制确保了强大的性能,即使传感器故障或信号丢失.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 导航系统 导航系统
背景情况:
- 自动驾驶汽车需要精确的状态估计才能安全运行.
- 像VIO和GNSS这样的当前本地化方法在不同的环境中面临挑战,包括城市峡谷和传感器故障.
- 准确的位置和方向估计对于本地和全球坐标系统至关重要.
研究的目的:
- 为自动驾驶汽车开发一种增强的地面车辆定位方法.
- 通过整合VIO和GNSS数据来提高状态估计的准确性和稳定性.
- 为应对VINS故障和GNSS信号退化所带来的挑战.
主要方法:
- 从视觉惯性测距 (VIO) 与全球导航卫星系统 (GNSS) 数据的局部估计的整合.
- 在姿势图中优化传感器融合,以获得精确的本地和无漂移的全球估计.
- 实现一个具有三个模式的自适应融合机制:仅VINS,仅GNSS和正常融合.
主要成果:
- 拟议的方法证明了强大而准确的本地化能力.
- 在卡拉模拟环境和具有挑战性的UrbanNav场景中成功验证.
- 定性和定量分析证实了算法的有效性.
结论:
- 增强的本地化方法为自动驾驶汽车提供了更好的可靠性.
- 适应性融合机制提供了应对传感器故障和环境限制的弹性.
- 这种方法有助于实现更安全,更可靠的自动驾驶系统.
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