This paper presents a hybrid method for single-source local- ization in wireless sensor networks, fusing noisy range mea- surements with angular information extracted from video. Although recent works found in the literature explore hybrid schemes, these include several cumbersome assumptions. We develop and test, both numerically and experimentally, a hy- brid localization algorithm which surpasses the limitations of previous fusing approaches. The proposed method (FLORIS) is based on a nonconvex least-squares joint formulation, for which a tight convex relaxation is applied to obtain a semidef- inite program. Numerical simulations show that FLORIS has comparable performance to state-of-the-art methods, even outperforming them in some scenarios. Real experiments show that FLORIS is feasible in practical application scenar- ios, achieving very good accuracy and robustness. Impor- tantly, coverage requirements for the infrastructure in a given area are more flexible than resorting to a single type of sensor, which may simplify practical deployments.