Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis

TL;DR

This paper introduces SceneSense, a diffusion model that predicts and fuses 3D occupancy maps in real-time, significantly improving robotic exploration, map quality, and traversability in diverse environments.

Contribution

The paper presents SceneSense, a novel generative diffusion model for occupancy mapping that enhances real-time robotic exploration and map quality.

Findings

SceneSense improves map fidelity with 24.44% FID reduction near the robot.

Enhanced maps lead to 75.59% better accuracy at range.

Integration of SceneSense enhances exploration robustness and efficiency.

Abstract

We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We implement SceneSense, a diffusion model designed and trained for predicting 3D occupancy maps given partial observations. Our proposed approach probabilistically fuses these predictions into a running occupancy map in real-time, resulting in significant improvements in map quality and traversability. We deploy SceneSense on a quadruped robot and validate its performance with real-world experiments to demonstrate the effectiveness of the model. In these experiments we show that occupancy maps enhanced with SceneSense predictions better estimate the distribution of our fully observed ground truth data ($24.44\%$ FID improvement around the robot and $75.59\%$ improvement at range). We additionally show that integrating SceneSense enhanced maps into our robotic exploration stack as a ``drop-in'' map improvement, utilizing an existing off-the-shelf planner, results in improvements in robustness and traversability time. Finally, we show results of full exploration evaluations with our proposed system in two dissimilar environments and find that locally enhanced maps provide more consistent exploration results than maps constructed only from direct sensor measurements.