RINO: Accurate, Robust Radar-Inertial Odometry with Non-Iterative Estimation
Shuocheng Yang · Yueming Cao · Shengbo Eben Li . jianQiang Wang* . Shaobing Xu*
## Abstract Precise localization and mapping are critical for achieving autonomous navigation in self-driving vehicles. However, ego-motion estimation still faces significant challenges, particularly when GNSS failures occur or under extreme weather conditions (e.g., fog, rain, and snow). In recent years, scanning radar has emerged as an effective solution due to its strong penetration capabilities. Nevertheless, scanning radar data inherently contains high levels of noise, necessitating hundreds to thousands of iterations of optimization to estimate a reliable transformation from the noisy data. Such iterative solving is time-consuming, unstable, and prone to failure. To address these challenges, we propose an accurate and robust Radar-Inertial Odometry system, RINO, which employs a non-iterative solving approach. Our method decouples rotation and translation estimation and applies an adaptive voting scheme for 2D rotation estimation, enhancing efficiency while ensuring consistent solving time. Additionally, the approach implements a loosely coupled system between the scanning radar and an inertial measurement unit (IMU), leveraging Error-State Kalman Filtering (ESKF). Notably, we successfully estimated the uncertainty of the pose estimation from the scanning radar, incorporating this into the filter's Maximum A Posteriori estimation, a consideration that has been previously overlooked. Validation on publicly available datasets demonstrates that RINO outperforms state-of-the-art methods and baselines in both accuracy and robustness. ## Overview  ## Getting Start ### Step-by-step installation instructions #### 1.Install Dependencies This project is developed based on ROS Noetic and is recommended to be compiled on Ubuntu 20.04. Please follow the steps below to install the necessary dependencies: ```bash sudo apt-get install libomp-dev sudo apt-get install libopencv-dev sudo apt-get install libeigen3-dev sudo apt-get install libboost-dev libboost-all-dev sudo apt-get install libpcl-dev ``` **Sophus**: a C++ library for Lie groups and Lie algebra operations. You need to download and build it manually from the official website: - Visit the official repository: https://github.com/strasdat/Sophus. Follow the installation instructions provided on the repository to compile and install Sophus. #### 2.Build RINO ```bash mkdir ~/catkin_ws/src cd ~/catkin_ws/src git clone https://github.com/yangsc4063/rino.git cd .. catkin_make ``` ### Prepare Dataset RINO is developed based on ROS and can read messages published by ROS nodes. To use RINO, simply publish your radar and IMU data as ROS topics. We recommend using the **File Player for MulRan Dataset** to simulate the input data. The MulRan dataset provides both radar and IMU data suitable for RINO’s input requirements. - You can find the **File Player for MulRan Dataset** here: https://github.com/RPM-Robotics-Lab/file_player_mulran. ### Run ```bash cd ~/catkin_ws source devel/setup.bash roslaunch rino rino.launch ``` ## Primary Results **Result on KAIST02 of MulRan dataset:**