Omead Amidi

Integrated mobile robot control

This paper describes the strucwre implementation and operation of a real-time mobile robot controller which integrates capabilities such as: position estimation path specification and hacking human interfaces fast communication and multiple client support The benefits of such high-level capabilities in a low-level controller was shown by its implementation for the Naviab autonomous vehicle. In addition performance results from positioning and tracking systems are reported and analyzed.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments

The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.

A visual odometer for autonomous helicopter flight

This paper presents a visual odometer for autonomous helicopter flight. The odometer estimates helicopter position by visually locking on to and tracking ground objects. The paper describes the philosophy behind the odometer as well as its tracking algorithm and implementation. The paper concludes by presenting test flight data of the odometers performance on-board indoor and outdoor prototype autonomous helicopters.

Real-time and 3D vision for autonomous small and micro air vehicles

Autonomous control of small and micro air vehicles (SMAV) requires precise estimation of both vehicle state and its surrounding environment. Small cameras, which are available today at very low cost, are attractive sensors for SMAV. 3D vision by video and laser scanning has distinct advantages in that they provide positional information relative to objects and environments, in which the vehicle operates, that is critical to obstacle avoidance and mapping of the environment. This paper presents work on real-time 3D vision algorithms for recovering motion and structure from a video sequence, 3D terrain mapping from a laser range finder onboard a small autonomous helicopter, and sensor fusion of visual and GPS/INS sensors.

An active camera system for acquiring multi-view video

A system is described for acquiring multi-view video of a person moving through the environment. A real-time tracking algorithm adjusts the pan, tilt, zoom and focus parameters of multiple active cameras to keep the moving person centered in each view. The output of the system is a set of synchronized, time-stamped video streams, showing the person simultaneously from several viewpoints.

Planning 3-D Path Networks in Unstructured Environments

In this paper, we explore the problem of three-dimensional motion planning in highly cluttered and unstructured outdoor environments. Because accurate sensing and modeling of obstacles is notoriously difficult in such environments, we aim to build computational tools that can handle large point data sets (e.g. LADAR data). Using a priori aerial data scans of forested environments, we compute a network of free space bubbles forming safe paths within environments cluttered with tree trunks, branches and dense foliage. The network (roadmap) of paths is used for efficiently planning paths that consider obstacle clearance information. We present experimental results on large point data sets typical of those faced by Unmanned Aerial Vehicles, but also applicable to ground-based robots navigating through forested environments.

Cascaded position and heading control of a robotic helicopter

We present a cascaded control architecture for a Yamaha RMAX robotic helicopter. The controller is composed of an inner-loop that stabilizes the unstable poles of the helicopters linear dynamic model; and an outer-loop that decouples the dynamics of the lateral, longitudinal, vertical, and heading axes and enables trajectory tracking. Actual flight results are presented to demonstrate the validity of the method. A discussion on the methods limitations and our plans on how to overcome them are also presented.

Toward laser pulse waveform analysis for scene interpretation

Laser based sensing for scene interpretation and obstacle detection is challenged by partially viewed targets, wiry structures, and porous objects. We propose to address such problems by looking at the laser pulse waveform. We designed a new laser sensor with off-the-shelf components. We report on the design and the evaluation of this low cost and compact sensor, suitable for mobile robot application. We determine classical parameters such as operation range, repeatability, accuracy, resolution, but we also analyze laser pulse waveforms modes and mode shape in order to extract additional information on the scene.

Precision 3-D Modeling for Autonomous Helicopter Flight

We present our primary research goals, which motivate the need for 3-D modeling as a key element for helicopter state estimation and situational awareness. We present a research system architecture, which integrates 3-D modeling and other on-board sensing to isolate the issues and requirements for our on-going research. Finally, we conclude by presenting preliminary modeling results and future research plans.

EigenFairing: 3D Model Fairing Using Image Coherence

A surface is often modeled as a triangulated mesh of 3D points and textures associated with faces of the mesh. The 3D points could be either sampled from range data or derived from a set of images using a stereo or Structurefrom-Motion algorithm. When the points do not lie at critical points of maximum curvature or discontinuities of the real surface, faces of the mesh do not lie close to the modeled surface. This results in textural artifacts, and the model is not perfectly coherent with a set of actual images—the ones that are used to texture-map its mesh. This paper presents a technique for perfecting the 3D surface model by repositioning its vertices so that it is coherent with a set of observed images of the object. The textural artifacts and incoherence with images are due to the non-planarity of a surface patch being approximated by a planar face, as observed from multiple viewpoints. Image areas from the viewpoints are used to represent texture for the patch in eigenspace. The eigenspace representation captures variations of texture, which we seek to minimize. A coherence measure based on the difference between the face textures reconstructed from eigenspace and the actual images is used to reposition the vertices so that the model is improved or faired. We refer to this technique of model refinement as EigenFairing, by which the model is faired, both geometrically and texturally, to better approximate the real surface.