4DRE-VIO: 4D Radar-Enhanced Monocular Visual Inertial Odometry for Urban Environments

Localization is crucial for Intelligent Transportation Systems (ITS) as it provides the precise position and orientation necessary for the operation of autonomous vehicles. Monocular vision-inertial odometry (VIO) has gained extensive application due to its high accuracy and cost-effectiveness. In this paper, we propose 4DRE-VIO, a novel 4D radar-enhanced monocular VIO system that achieves accurate and cost-effective pose estimation for autonomous vehicles in urban environments. By tightly integrating 4D radar measurements, we address the issues of scale ambiguity, scale drift, and dynamic object interference associated with monocular VIO. Specifically, we integrate 4D radar Doppler velocity with non-holonomic constraints (NHC) to provide a reliable velocity observation. Moreover, this velocity is directly used to construct a monocular scale observation, ensuring stable and accurate scale estimation. To mitigate the impact of dynamic objects on VIO, which is based on static environment assumptions, we introduce a 4D radar-enhanced visual semantic segmentation algorithm for dynamic object recognition. Additionally, we utilize a factor graph to tightly integrate measurements from the 4D radar, IMU, and monocular camera. The design of adjacent factors and optimal co-visible factors ensures the efficient use of visual information. An adaptive fusion strategy based on driving conditions maximizes the benefits of each sensor. We validate the effectiveness of the proposed method through extensive testing on a large dataset collected in urban environments, including open streets and residential districts. Compared to typical VIO and 4D radar-related odometry, our method demonstrates superior performance on our dataset and the public dataset.