We present MIDGARD, an open-source simulation platform for autonomous robot
navigation in outdoor unstructured environments. MIDGARD is designed to enable
the training of autonomous agents (e.g., unmanned ground vehicles) in
photorealistic 3D environments, and to support the generalization skills of
learning-based agents through the variability in training scenarios. MIDGARD's
main features include a configurable, extensible, and difficulty-driven
procedural landscape generation pipeline, with fast and photorealistic scene
rendering based on Unreal Engine. Additionally, MIDGARD has built-in support
for OpenAI Gym, a programming interface for feature extension (e.g.,
integrating new types of sensors, customizing exposing internal simulation
variables), and a variety of simulated agent sensors (e.g., RGB, depth and
instance/semantic segmentation). We evaluate MIDGARD's capabilities as a
benchmarking tool for robot navigation utilizing a set of state-of-the-art
reinforcement learning algorithms. The results demonstrate MIDGARD's
suitability as a simulation and training environment, as well as the
effectiveness of our procedural generation approach in controlling scene
difficulty, which directly reflects on accuracy metrics. MIDGARD build, source
code and documentation are available at https://midgardsim.org/