Tightly-Coupled Multi-Sensor Fusion for Localization with LiDAR Feature Maps

Robust and accurate pose estimation in long-term localization is crucial to autonomous driving. In this paper, we dealt with absolute localization with a LiDAR feature map and multi-sensor measurements. We proposed a tightly-coupled fusion method with fixed-lag smoothing. A sliding window of recently maintained states is estimated by minimizing a joint cost function. This cost function includes residuals of global LiDAR registration and relative kinematic constraints from an IMU and wheel encoders. In addition, we enhance the robustness of our method by improving LiDAR registration. To achieve this goal, LiDAR feature maps with a hybrid of geometric and normal distribution features are constructed and exploited. The effectiveness of the proposed method is verified in several challenging test sequences over 200km. The experimental results demonstrate that the proposed method achieves accurate localization and high robustness in challenging scenarios even when the LiDAR observation is degraded.