  We present a framework for a ground-aerial robotic team to explore large,
unstructured, and unknown environments. In such exploration problems, the
effectiveness of existing exploration-boosting heuristics often scales poorly
with the environments' size and complexity. This work proposes a novel
framework combining incremental frontier distribution, goal selection with
Monte-Carlo view quality rendering, and an automatic-differentiable information
gain measure to improve exploration efficiency. Simulated with multiple complex
environments, we demonstrate that the proposed method effectively utilizes
collaborative aerial and ground robots, consistently guides agents to
informative viewpoints, improves exploration paths' information gain, and
reduces planning time.
