This paper proposes an end-to-end radio simultaneous localization and mapping (SLAM) algorithm that directly leverages channel impulse response (CIR) to overcome fundamental limitations in existing approaches. Traditional radio SLAM algorithms assume pre-estimated channel parameters, making performance highly sensitive to estimation accuracy, while recent end-to-end methods jointly perform parameter estimation and SLAM but suffer from high computational complexity and model mismatch vulnerability. The proposed algorithm minimizes information loss by operating directly on raw CIR measurements and utilizes end-to-end learning for enhanced robustness. Simulation results in the 3GPP TR 38.857 indoor factory scenario demonstrate that the proposed algorithm achieves comparable performance to conventional radio SLAM while reducing computational time by less than $2\%$, confirming its strong potential for practical deployment.