Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes

By Guowei Wan, Xiaolong Yang, Renlan Cai, Hao Li, Hongya Wang, Shiyu Song. IEEE International Conference on Robotics and Automation, 2017

321 citations recorded by the source corpus.

We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fusion framework and achieves a better ambiguity resolution success rate. An error-state Kalman filter is applied to fuse the localization measurements from different sources with novel uncertainty estimation. We validate, in detail, the effectiveness of our approaches, achieving 5-10cm RMS accuracy and outperforming previous state-of-the-art systems. Importantly, our system, while deployed in a large autonomous driving fleet, made our vehicles fully autonomous in crowded city streets despite road construction that occurred from time to time. A dataset including more than 60 km real traffic driving in various urban roads is used to comprehensively test our system.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e20afec18cf0d2d8b5ceeb1792ff26b01b55ae00

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Pengfei Yuan
2. Yao Zhou
3. Shiyu Song
4. Guowei Wan
5. Weixin Lu

In Artificial Intelligence

1. Yao Zhou
2. Shiyu Song
3. Guowei Wan

In Electrical and Electronic Engineering

1. Yao Zhou
2. Shiyu Song
3. Guowei Wan
4. Weixin Lu

Citations in the corpus

• DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration (2019, background): “It is a well-known problem and plays an essential role in many applications, such as LiDAR SLAM [50, 8, 19, 27], 3D reconstruction and mapping [38, 10, 45, 9], positioning and localization [48, 20, 42, 25], object pose estimation [43] and so on.”