Compared to conventional 3D radar, the 4D imaging radar provides additional height data and finer resolution measurements. Moreover, compared to LiDAR sensors, 4D imaging radar is more cost-effective and offers enhanced durability against challenging weather conditions. Despite these advantages, radar-based localization systems face several challenges, including limited resolution, leading to scattered object recognition and less precise localization. Additionally, existing methods that form submaps from filtered results can accumulate errors, leading to blurred submaps and reducing the accuracy of the SLAM and odometry. To address these challenges, this paper introduces Radar4VoxMap, a novel approach designed to enhance radar-only odometry. The method includes an RCS-weighted voxel distribution map that improves registration accuracy. Furthermore, fixed-lag optimization with the graph is used to optimize both the submap and pose, effectively reducing cumulative errors. The proposed method has shown strong performance on open datasets. The code is available at: https://github.com/ailab-hanyang/Radar4VoxMap

The importance of precise localization technology for the autonomous driving of industrial mobile robots is steadily increasing. Notably, research into enhancing accuracy and robustness by fusing multiple systems is actively conducted rather than relying on a single localization system. We highlight the use of track-to-track (T2T) fusion, which takes the localization results of independent systems as input. This approach eliminates system adjustments with sensor changes, offering benefits for industrial mobile robots. However, existing T2T-based fusion methods suffer from overlooking slowly changing biases that can gradually increase over time due to sensor drift errors, map biases, etc. Since biases have different values and frequencies for each system, they are challenging for conventional T2T methods to handle. This article proposes a localization fusion framework that tackles such slowly varying biases. First, estimating the distinct biases inherent to each system poses a challenging problem; therefore, we align them to a single common bias. Second, localization estimates with a common bias are fused using a split covariance intersection filter, one of the T2T fusion techniques, considering the independence and correlation within each system to ensure fusion consistency. The proposed method has been validated in both simulation and real-world environments, confirming superior performance compared to existing algorithms.

The development of autonomous vehicles has been accelerating, marked by a variety of competitions that challenge teams with diverse missions. Recently, racing-based autonomous driving competitions have gained prominence. Notably, the 2023 Hyundai Motor Group Autonomous Driving Challenge (HMG ADC) stands out as a manufacturer-operated event with a racing concept. This competition was distinctive, featuring mass-produced vehicles on race track with multiple vehicles simultaneously. In this paper, we explore the AutoKU team’s participation in the HMG ADC, highlighting their system, which is designed for two types of driving: solo and multi-vehicle racing. We detail the use of an identical mass-produced Hyundai IONIQ 5 vehicle equipped for autonomous driving without any performance modifications. The paper will discuss AutoKU’s approach and performance in solo and multi-vehicle races, showcasing their strategies and achievements in this innovative autonomous racing challenge. (Video: https://youtu.be/wLtmUkahnYA?si=AjqH6hYe10O94laq).