Charles Q. Little

Registration of range data using a hybrid simulated annealing and iterative closest point algorithm

The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.

Rapid world modeling: fitting range data to geometric primitives

World modeling is defined as the process of creating a numerical geometric model of a real world environment or workspace. This model is often used in robotics to plan robot motions which perform a task while avoiding obstacles. In many applications where the world model does not exist ahead of time, structured lighting, laser range finders, and even acoustical sensors have been used to create three dimensional maps of the environment. These maps consist of thousands of range points which are difficult to handle and interpret. This paper presents a least squares technique for fitting range data to planar and quadric surfaces, including cylinders and ellipsoids. Once fit to these primitive surfaces, the amount of data associated with a surface is greatly reduced up to three orders of magnitude, thus allowing for more rapid handling and analysis of world data.

Real-Time Tracking of Articulated Human Models Using a 3D Shape-from-Silhouette Method

This paper describes a system, which acquires 3D data and tracks an eleven degree of freedom human model in real-time. Using four cameras we create a time-varying volumetric image (a visual hull) of anything moving in the space observed by all four cameras. The sensor is currently operating in a volume of approximately 500,000 voxels (1.5 inch cubes) at a rate of 25 Hz. The system is able to track the upper body dynamics of a human (x,y position of the body, a torso rotation, and four rotations per arm). Both data acquisition and tracking occur on one computer at a rate of 16 Hz. We also developed a calibration procedure, which allows the system to be moved and be recalibrated quickly. Furthermore we display in real-time, either the data overlaid with the joint locations or a human avatar. Lastly our system has been implemented to perform crane gesture recognition.

Rapid world modelling from a mobile platform

The ability to successfully use and interact with a computerized world model is dependent on the ability to create an accurate world model. The goal of this project was to develop a prototype system to remotely deploy sensors into a workspace, collect surface information, and rapidly build an accurate world model of that workspace. A key consideration was that the workspace areas are typically hazardous environments, where it is difficult or impossible for humans to enter. Therefore, the system needed to be fully remote, with no external connections. To accomplish this goal, an electric, mobile platform with battery power sufficient for both the platform and sensor electronics was procured and 3D range sensors were deployed on the platform to capture surface data within the workspace. A radio Ethernet connection was used to provide communications to the vehicle and all on-board electronics. Video from on-board cameras was also transmitted to the base station and used to teleoperate the vehicle. Range data generated by the on-board 3D sensors was transformed into surface maps, or models. Registering the sensor location to a consistent reference frame as the platform moved through the workspace allowed construction of a detailed 3D world model of the extended workspace.

Forensic 3D Scene Reconstruction

Traditionally law enforcement agencies have relied on basic measurement and imaging tools, such as tape measures and cameras, in recording a crime scene. A disadvantage of these methods is that they are slow and cumbersome. The development of a portable system that can rapidly record a crime scene with current camera imaging, 3D geometric surface maps, and contribute quantitative measurements such as accurate relative positioning of crime scene objects, would be an asset to law enforcement agents in collecting and recording significant forensic data. The purpose of this project is to develop a fieldable prototype of a fast, accurate, 3D measurement and imaging system that would support law enforcement agents to quickly document and accurately record a crime scene.

The Sandia architecture for heterogeneous unmanned system control (SAHUC)

The Sandia Architecture for Heterogeneous Unmanned System Control (SAHUC) was produced as part of a three year internally funded project performed by Sandia’s Intelligent Systems, Robotics, and Cybernetics group (ISRC). ISRC created SAHUC to demonstrate how teams of Unmanned Systems (UMS) can be used for small-unit tactical operations incorporated into the protection of high-consequence sites. Advances in Unmanned Systems have provided crucial autonomy capabilities that can be leveraged and adapted to physical security applications. SAHUC applies these capabilities to provide a distributed ISR network for site security. This network can be rapidly re-tasked to respond to changing security conditions. The SAHUC architecture contains multiple levels of control. At the highest level a human operator inputs objectives for the network to accomplish. The heterogeneous unmanned systems automatically decide which agents can perform which objectives and then decide the best global assignment. The assignment algorithm is based upon coarse metrics that can be produced quickly. Responsiveness was deemed more crucial than optimality for responding to time-critical physical security threats. Lower levels of control take the assigned objective, perform online path planning, execute the desired plan, and stream data (LIDAR, video, GPS) back for display on the user interface. SAHUC also retains an override capability, allowing the human operator to modify all autonomous decisions whenever necessary. SAHUC has been implemented and tested with UAVs, UGVs, and GPS-tagged blue/red force actors. The final demonstration illustrated how a small fleet, commanded by a remote human operator, could aid in securing a facility and responding to an intruder.

A layered control architecture for single-operator control of heterogeneous unmanned system teams

The widespread adoption of aerial, ground and sea-borne unmanned systems (UMS) for national security applications provides many advantages; however, effectively controlling large numbers of UMS in complex environments with modest manpower is a significant challenge. A control architecture and associated control methods are under development to allow a single user to control a team of multiple heterogeneous UMS as they conduct multi-faceted (i.e. multi-objective) missions in real time. The control architecture is hierarchical, modular and layered and enables operator interaction at each layer, ensuring the human operator is in close control of the unmanned team at all times. The architecture and key data structures are introduced. Two approaches to distributed collaborative control of heterogeneous unmanned systems are described, including an extension of homogeneous swarm control and a novel application of distributed model predictive control. Initial results are presented, demonstrating heterogeneous UMS teams conducting collaborative missions. Future work will focus on interacting with dynamic targets, integrating alternative control layers, and enabling a deeper and more intimate level of real-time operator control.

Simulated Mobile Self-Location Using 3D Range Sensing and an A-Priori Map

Self-location is a critical component of unmanned ground vehicle operation. While GPS has met this need for many applications, there are others, indoors and outdoors, where GPS is not available or reliable. This paper discusses the investigation of a method to self-locate using an onboard 3D range sensor, when a 3D map of the environment is available. We have examined the performance in terms of what is needed for data coverage and terrain types. Localization is accomplished through surface fitting. Surface fitting is an important component of Simultaneous Localization and Mapping (SLAM) for 3D terrains. Three environments were chosen for testing, including a hilly desert scene, an indoor corridor scene, and a forest scene. A simulated 3D range sensor was used to scan models corresponding to these three environments. Various parameters were modified to identify strong and weak points in the scheme.

Enhanced Physical Security through a Command-Intent Driven Multi-Agent Sensor Network.

Sandia’s Intelligent Systems, Robotics, and Cybernetics group (ISRC) created the Sandia Architecture for Heterogeneous Unmanned System Control (SAHUC) to demonstrate how heterogeneous multi-agent teams could be used for tactical operations including the protection of high-consequence sites. Advances in multi-agent autonomy and unmanned systems have provided revolutionary new capabilities that can be leveraged for physical security applications. SAHUC applies these capabilities to produce a command-intent driven, autonomously adapting, multi-agent mobile sensor network. This network could enhance the security of high-consequence sites; it can be quickly and intuitively re-tasked to rapidly adapt to changing security conditions.