Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
This addresses robotic mapping and navigation in unknown environments, representing an incremental improvement through generative model integration.
The paper tackles robotic exploration by using a diffusion model called SceneSense to predict 3D occupancy maps from partial observations, which improves map quality with a 24.44% FID improvement near the robot and 75.59% at range, and enhances exploration robustness and traversability time.
We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We introduce 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 implement SceneSense onboard 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 represent 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.