The development process of high fidelity SLAM systems depends on their validation upon reliable datasets. Towards this goal, we propose IBISCape, a simulated benchmark that includes data synchronization and acquisition APIs for telemetry from heterogeneous sensors: stereo-RGB/DVS, LiDAR, IMU, and GPS, along with the ground truth scene segmentation, depth maps and vehicle ego-motion. Our benchmark is built upon the CARLA simulator, whose back-end is the Unreal Engine rendering a high dynamic scenery simulating the real world. Moreover, we offer 43 datasets for Autonomous Ground Vehicles (AGVs) reliability assessment, including scenarios for scene understanding evaluation like accidents, along with a wide range of frame quality based on a dynamic weather simulation class integrated with our APIs. We also introduce the first calibration targets to CARLA maps to solve the unknown distortion parameters problem of CARLA simulated DVS and RGB cameras. Furthermore, we propose a novel pre-processing layer that eases the integration of DVS sensor events in any frame-based Visual-SLAM system. Finally, extensive qualitative and quantitative evaluations of the latest state-of-the-art Visual/Visual-Inertial/LiDAR SLAM systems are performed on various IBISCape sequences collected in simulated large-scale dynamic environments.