HGCM: A hybrid radar feature and GNSS based continuous mapping method

• GNSS significantly enhances the accuracy of radar SLAM in urban scenarios. • Addressing over-saturation and occlusion helps to increase system stability. • Radar constraints can mitigate GNSS noise, thus ensuring global consistency. Online mapping and localization are critical technologies in autonomous driving and robotics systems, currently being a prominent research topic. However, the majority of proposed methods have primarily focused on camera and LiDAR (Light Detection and Ranging) sensors, which are inadequate for addressing more challenging weather conditions. Radar-based simultaneous localization and mapping (SLAM) is essential for autonomous navigation in complex and dynamic environments where other sensors may fail. However, there is a gap in accuracy and stability compared with LiDAR-based SLAM, thus limiting its application for autonomous driving, drones and robotics. High noise levels, low feature stability and the lack of global constraints would result in the deterioration of pose estimation. This paper presents a Simultaneous Localization and Mapping (SLAM) system that combines hybrid features from a spinning radar and Global Navigation Satellite System (GNSS) measurements to achieve accurate mapping results in complex urban environments. To ensure intra-frame and inter-frame stability of pose estimation, we applied structural noise removal, feature ranking and motion buffer techniques. Focusing on improving trajectory consistency, we introduce a GNSS factor and a motion factor to provide stable global constraints and mitigate the impact of GNSS noise on the system, respectively. Experimental results on the Mulran dataset, which includes complex urban scenarios, demonstrate that our system achieves state-of-the-art performance, with an average ATE(Absolute Trajectory Error) of 2.71m and a standard deviation of 1.10m.