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The development process of high-fidelity SLAM systems depends on theirvalidation upon reliable datasets. Towards this goal, we propose IBISCape, asimulated benchmark that includes data synchronization and acquisition APIs fortelemetry from heterogeneous sensors: stereo-RGB/DVS, Depth, IMU, and GPS,along with the ground truth scene segmentation and vehicle ego-motion. Ourbenchmark is built upon the CARLA simulator, whose back-end is the UnrealEngine rendering a high dynamic scenery simulating the real world. Moreover, weoffer 34 multi-modal datasets suitable for autonomous vehicles navigation,including scenarios for scene understanding evaluation like accidents, alongwith a wide range of frame quality based on a dynamic weather simulation classintegrated with our APIs. We also introduce the first calibration targets toCARLA maps to solve the unknown distortion parameters problem of CARLAsimulated DVS and RGB cameras. Finally, using IBISCape sequences, we evaluatefour ORB-SLAM3 systems (monocular RGB, stereo RGB, Stereo Visual Inertial(SVI), and RGB-D) performance and BASALT Visual-Inertial Odometry (VIO) systemon various sequences collected in simulated large-scale dynamic environments.  Keywords: benchmark, multi-modal, datasets, Odometry, Calibration, DVS, SLAM

IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments