In this paper, we propose an end-to-end approach to endow indoor service robots with the ability to avoid collisions using Deep Reinforcement Learning (DRL). The proposed method allows a controller to derive continuous velocity commands for an omnidirectional mobile robot using depth images, laser measurements, and odometry based speed estimations. The controller is parameterized by a deep neural network, and trained using DDPG. To improve the limited perceptual range of most indoor robots, a method to exploit range measurements through sensor integration and feature extraction is developed. Additionally, to alleviate the reality gap problem due to training in simulations, a simple processing pipeline for depth images is proposed. As a case study we consider indoor collision avoidance using the Pepper robot. Through simulated testing we show that our approach is able to learn a proficient collision avoidance policy from scratch. Furthermore, we show empirically the generalization capabilities of the trained policy by testing it in challenging real-world environments. Videos showing the behavior of agents trained using the proposed method can be found at https://youtu.be/ypC39m4BlSk.