Robust 4D Radar Odometry With Heatmap Feature Encoding and Spatiotemporal Attention Network

Accurate odometry estimation is fundamental to autonomous navigation, enabling precise localization and continuous motion tracking. While vision-based and lidar-based odometry systems have reached a high level of maturity, their performance deteriorates significantly in adverse environmental conditions, such as low visibility, inclement weather, or dynamic lighting. In contrast, 4D radar offers inherent robustness to such challenges, along with extended range and velocity-sensing capabilities, making it a compelling alternative for long-term, all-weather perception. In this paper, we present an end-to-end neural network-based odometry framework that directly leverages raw ADC data from 4D radars, surpassing conventional methods that are limited to processing only 4D point cloud information. We introduce a lossless encoding scheme that converts the raw ADC signals into structured heatmap representations, preserving essential spatial and temporal information. To extract meaningful features from this rich representation, we design a hybrid attention architecture that combines intra-frame self-attention for enhanced spatial understanding with inter-frame cross-attention to model fine-grained temporal dependencies. Comprehensive evaluations on the Coloradar dataset show that our method significantly outperforms state-of-the-art radar-based odometry approaches based on traditional feature matching or deep learning. It achieves superior accuracy and robustness, highlighting its strong potential for real-world deployment in autonomous navigation systems under challenging conditions. Our code is available at https://github.com/MoYuGit/Deep_Radar_Odometry.