Despite the recent achievements in stable dynamic walking for many humanoid robots, relatively little navigation autonomy has been achieved. In particular, the ability to autonomously select foot placement positions to avoid obstacles while walking is an important step towards improved navigation autonomy for humanoids. We present a footstep planner for the Honda ASIMO humanoid robot that plans a sequence of footstep positions to navigate toward a goal location while avoiding obstacles. The possible future foot placement positions are dependent on the current state of the robot. Using a finite set of state-dependent actions, we use an A* search to compute optimal sequences of footstep locations up to a time-limited planning horizon. We present experimental results demonstrating the robot navigating through both static and dynamic known environments that include obstacles moving on predictable trajectories.

Shape-from-silhouette (SFS), also known as visual hull (VH) construction, is a popular 3D reconstruction method, which estimates the shape of an object from multiple silhouette images. The original SFS formulation assumes that the entire silhouette images are captured either at the same time or while the object is static. This assumption is violated when the object moves or changes shape. Hence the use of SFS with moving objects has been restricted to treating each time instant sequentially and independently. Recently we have successfully extended the traditional SFS formulation to refine the shape of a rigidly moving object over time. We further extend SFS to apply to dynamic articulated objects. Given silhouettes of a moving articulated object, the process of recovering the shape and motion requires two steps: (1) correctly segmenting (points on the boundary of) the silhouettes to each articulated part of the object, (2) estimating the motion of each individual part using the segmented silhouette. In this paper, we propose an iterative algorithm to solve this simultaneous assignment and alignment problem. Once we have estimated the shape and motion of each part of the object, the articulation points between each pair of rigid parts are obtained by solving a simple motion constraint between the connected parts. To validate our algorithm, we first apply it to segment the different body parts and estimate the joint positions of a person. The acquired kinematic (shape and joint) information is then used to track the motion of the person in new video sequences.

Visual hull (VH) construction from silhouette images is a popular method of shape estimation. The method, also known as shape-from-silhouette (SFS), is used in many applications such as non-invasive 3D model acquisition, obstacle avoidance, and more recently human motion tracking and analysis. One of the limitations of SFS, however, is that the approximated shape can be very coarse when there are only a few cameras. In this paper, we propose an algorithm to improve the shape approximation by combining multiple silhouette images captured across time. The improvement is achieved by first estimating the rigid motion between the visual hulls formed at different time instants (visual hull alignment) and then combining them (visual hull refinement) to get a tighter bound on the object's shape. Our algorithm first constructs a representation of the VHs called the bounding edge representation. Utilizing a fundamental property of visual hulls, which states that each bounding edge must touch the object at at least one point, we use multi-view stereo to extract points called colored surface points (CSP) on the surface of the object. These CSPs are then used in a 3D image alignment algorithm to find the 6 DOF rigid motion between two visual hulls. Once the rigid motion across time is known, all of the silhouette images are treated as being captured at the same time instant and the shape of the object is refined. We validate our algorithm on both synthetic and real data and compare it with space carving.

While bitrate-reduction video transcoding is a mature research topic, we introduce a new paradigm for the old problem: designing a complexity scalable transcoder where computation can be gracefully traded off with transcoded video quality. Complexity scalability is important when a time-varying number of real-time transcoding sessions must be maintained by the transcoder simultaneously. We present a complexity scalable transcoding algorithm for H.263 based on coding mode transition probabilities. Preliminary results are presented to show potential tradeoffs between computation and transcoded video quality in PSNR.

We present a multi-PC/camera system that can perform 3D reconstruction and ellipsoid fitting of moving humans in real time. The system consists of five cameras. Each camera is connected to a PC which locally extracts the silhouettes of the moving person in the image captured by the camera. The five silhouette images are then sent, via local network, to a host computer to perform 3D voxel-based reconstruction by an algorithm called SPOT. Ellipsoids are then used to fit the reconstructed data. By using a simple and user-friendly interface, the user can display and observe, in real time and from any view-point, the 3D models of the moving human body. With a rate of higher than 15 frames per second, the system is able to capture non-intrusively, a sequence of human motions.