Humanoid robots possess the ability to perform complex tasks in challenging environments. However, they require a model of the surroundings in a representation that is sufficient enough for downstream tasks such as footstep planning. The maps generated by existing mapping algorithms are either sparse, insufficient for footstep planning, memory intensive, or too slow for dynamic humanoid behaviors. In this work, we develop a mapping algorithm that combines planar region measurements along with kinematic-inertial state estimates to build a dense but efficient map of bounded planar surfaces. We present novel algorithms for plane feature matching, tracking and registration for mapping within a factor graph framework. The generated map is not only memory efficient, but also offers higher reliability and speed in bipedal footstep planning, than was possible earlier. The complete algorithm is also demonstrated using a full-scale humanoid robot, Nadia, walking over both flat ground and rough terrain utilizing the generated terrain map.

For achieving significant levels of autonomy, legged robot behaviors require perceptual awareness of both the terrain for traversal, as well as structures and objects in their surroundings for planning, obstacle avoidance, and high-level decision making. In this work, we present a perception engine for legged robots that extracts the necessary information for developing semantic, contextual, and metric awareness of their surroundings. Our custom sensor configuration consists of (1) an active depth sensor, (2) two monocular cameras looking sideways, (3) a passive stereo sensor observing the terrain, (4) a forward facing active depth camera, and (5) a rotating 3D LIDAR with a large vertical field-of-view (FOV). The mutual overlap in the sensors' FOVs allows us to redundantly detect and track objects of both dynamic and static types. We fuse class masks generated by a semantic segmentation model with LIDAR and depth data to accurately identify and track individual instances of dynamically moving objects. In parallel, active depth and passive stereo streams of the terrain are also fused to map the terrain using the on-board GPU. We evaluate the engine using two different humanoid behaviors, (1) look-and-step and (2) track-and-follow, on the Boston Dynamics Atlas.

Legged robots require fast and accurate representation of their surrounding terrain to achieve behaviors such as running, push recovery, continuous walking, backflips, while also utilizing on-board computational resources efficiently. The desired tasks can be achieved efficiently by representing the environment using planar regions. However, existing methods for planar region extraction are either too slow or require significant compute time on the Central Processing Unit (CPU). In this work we exploit key properties of depth images and Graphical Processing Unit (GPU) to estimate planar regions around the robot at very high frame rates of 150-200 Hz. The proposed algorithm uses a set of fully customizable and interchangeable set of kernel layers on the GPU to process the depth map in parallel and generate a locally connected graph structure, which is later separated into planar components using a basic depth-first search. We test the proposed algorithm on the Atlas robot while performing different walking behaviors on oriented cinder blocks, as well as in simulation with simulated sensor and robot. The algorithm is open-sourced for research on legged robots and other fields.

Awareness of the environment is essential for mobile robots. Perception for legged robots requires high levels of reliability and accuracy in order to walk stably in the types of complex, cluttered environments we are interested in. In this paper, we present a usable environmental perception algorithm designed to detect steppable areas and obstacles for the autonomous generation of desired footholds for legged robots. To produce an efficient representation of the environment, the proposed perception algorithm is desired to cluster point cloud data to planar regions composed of convex polygons. We describe in this paper the end-to-end pipeline from data collection to generation of the regions, where we first compose an octree in order to create a more efficient data representation. We then group the leaves in the tree using a nearest neighbor search into a planar region, which is composed of the concave hull of points that is decomposed into convex polygons. We present a variety of environments, and illustrate the usability of this approach by the Atlas humanoid robots walking over rough terrain. We also discuss various challenges we faced and insights we gained in the development of this approach.