Curved patch mapping and tracking for irregular terrain modeling: Application to bipedal robot foot placement
This addresses the challenge of enabling legged robots to operate safely on unstructured natural terrains, though it appears incremental as it builds on existing perception and mapping techniques.
The paper tackles the problem of modeling irregular terrain for bipedal robot foot placement by introducing a real-time framework that uses parametrized patch models and a mapping system to identify potential contacts, with experimental validation on a mini-biped robot navigating rocky surfaces.
Legged robots need to make contact with irregular surfaces, when operating in unstructured natural terrains. Representing and perceiving these areas to reason about potential contact between a robot and its surrounding environment, is still largely an open problem. This paper introduces a new framework to model and map local rough terrain surfaces, for tasks such as bipedal robot foot placement. The system operates in real-time, on data from an RGB-D and an IMU sensor. We introduce a set of parametrized patch models and an algorithm to fit them in the environment. Potential contacts are identified as bounded curved patches of approximately the same size as the robot's foot sole. This includes sparse seed point sampling, point cloud neighborhood search, and patch fitting and validation. We also present a mapping and tracking system, where patches are maintained in a local spatial map around the robot as it moves. A bio-inspired sampling algorithm is introduced for finding salient contacts. We include a dense volumetric fusion layer for spatiotemporally tracking, using multiple depth data to reconstruct a local point cloud. We present experimental results on a mini-biped robot that performs foot placements on rocks, implementing a 3D foothold perception system, that uses the developed patch mapping and tracking framework.