MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

I. INTRODUCTION

The Simultaneous Localization and Mapping (SLAM) system [1]–[5] is the core of many robot-related tasks, such as robotic exploration and inspection [6]–[9]. The tightly coupled SLAM systems based on 3D Light Detection and Ranging (Lidar) and Inertial Measurement Unit (IMU) sensors have gained popularity due to high accuracy and robustness. In general circumstances, the systems employing an optimization framework often suffer from high computational cost, which limits their real-time applicability. To address this problem, utilizing Kalman Filter [10]–[12] for fusing Lidar and IMU data can significantly enhance calculation efficiency and increase the odometry output rate.