While recent low-cost radar-camera approaches have shown promising results inmulti-modal 3D object detection, both sensors face challenges from environmen-tal and intrinsic disturbances. Poor lighting or adverse weather conditions de-grade camera performance, while radar suffers from noise and positional ambigu-ity. Achieving robust radar-camera 3D object detection requires consistent perfor-mance across varying conditions, a topic that has not yet been fully explored. Inthis work, we first conduct a systematic analysis of robustness in radar-camera de-tection on five kinds of noises and propose RobuRCDet, a robust object detectionmodel in bird’s eye view (BEV). Specifically, we design a 3D Gaussian Expan-sion (3DGE) module to mitigate inaccuracies in radar points, including position,Radar Cross-Section (RCS), and velocity. The 3DGE uses RCS and velocity priorsto generate a deformable kernel map and variance for kernel size adjustment andvalue distribution. Additionally, we introduce a weather-adaptive fusion module,which adaptively fuses radar and camera features based on camera signal confi-dence. Extensive experiments on the popular benchmark, nuScenes, show thatour RobuRCDet achieves competitive results in regular and noisy conditions. Thesource codes and trained models will be made available.