In this work, we present a low-complexity photoplethysmography-based respiratory rate monitoring (PPG-RRM) algorithm that achieves high accuracy through a novel fusion method. The proposed technique extracts three respiratory-induced variation signals, namely the maximum slope, the amplitude, and the frequency, from the PPG signal. The variation signals undergo time domain peak detection to identify the inter-breath intervals and produce three different instantaneous respiratory rate (IRR) estimates. The IRR estimates are combined through a hybrid vote-aggregate fusion scheme to generate the final RR estimate. We utilize the publicly available Capnobase data-sets [1] that contain both PPG and capnography signals to evaluate our RR monitoring algorithm. Compared to the reference capnography IRR, the proposed PPG-RRM algorithm achieves a mean absolute error (MAE) of 1.44 breaths per minute (bpm), a mean error (ME) of 0.70±2.54 bpm, a root mean square error (RMSE) of 2.63 bpm, and a Pearson correlation coefficient r = 0.95, p < .001.