Probabilistic Terrain Analysis Using Semantic Criteria for Legged Robot Locomotion

In legged robotics locomotion, extracting comprehensive local information from terrain is essential for generating specific leg motions and navigating through unstructured areas. This involves identifying the environment and obstacles, thoroughly characterizing these elements, and defining the best areas to place the legs. Most state-of-the-art methods focus on navigating unstructured terrain only using height analysis, which, although reliable, does not consider the steadiness of the elements of the ground. This paper aims to enhance legged robot motion in unstructured terrain by precisely defining stability zones and leg support points. The primary method for obstacle identification and optimal foothold selection relies on a semantic-based criterion that considers the stability probabilities of each terrain element. A CNN has been trained to address probabilistic characterization. For applicability in a quadrupedal robot, methodology includes discretizing image regions, grouping pixels according to detections, associating discretized regions with the actual depth of the environment, and transforming coordinate systems from RGB-D camera to world-robot. Algorithms of the proposed method are found in the authors' GitHub repository.