  Simulation engines are widely adopted in robotics. However, they lack either
full simulation control, ROS integration, realistic physics, or photorealism.
Recently, synthetic data generation and realistic rendering has advanced tasks
like target tracking and human pose estimation. However, when focusing on
vision applications, there is usually a lack of information like sensor
measurements or time continuity. On the other hand, simulations for most
robotics tasks are performed in (semi)static environments, with specific
sensors and low visual fidelity. To solve this, we introduced in our previous
work a fully customizable framework for generating realistic animated dynamic
environments (GRADE) [1]. We use GRADE to generate an indoor dynamic
environment dataset and then compare multiple SLAM algorithms on different
sequences. By doing that, we show how current research over-relies on known
benchmarks, failing to generalize. Our tests with refined YOLO and Mask R-CNN
models provide further evidence that additional research in dynamic SLAM is
necessary. The code, results, and generated data are provided as open-source at
https://eliabntt.github.io/grade-rrSimulation of Dynamic Environments for SLAM
