Jason Ziglar

A Robust Approach to High-Speed Navigation for Unrehearsed Desert Terrain

This article presents a robust approach to navigating at high-speed across desert terrain. A central theme of this approach is the combination of simple ideas and components to build a capable and robust system. A pair of robots were developed which completed a 212 kilometer Grand Challenge desert race in approximately seven hours. A path-centric navigation system uses a combination of LIDAR and RADAR based perception sensors to traverse trails and avoid obstacles at speeds up to 15m/s. The onboard navigation system leverages a human based pre-planning system to improve reliability and robustness. The robots have been extensively tested, traversing over 3500 kilometers of desert trails prior to completing the challenge. This article describes the mechanisms, algorithms and testing methods used to achieve this performance.

Real-time photorealistic virtualized reality interface for remote mobile robot control

The task of teleoperating a robot over a wireless video link is known to be very difficult. Teleoperation becomes even more difficult when the robot is surrounded by dense obstacles, or speed requirements are high, or video quality is poor, or wireless links are subject to latency. Due to high-quality lidar data, and improvements in computing and video compression, virtualized reality has the capacity to dramatically improve teleoperation performance — even in high-speed situations that were formerly impossible. In this paper, we demonstrate the conversion of dense geometry and appearance data, generated on-the-move by a mobile robot, into a photorealistic rendering model that gives the user a synthetic exterior line-of-sight view of the robot, including the context of its surrounding terrain. This technique converts teleoperation into virtual line-of-sight remote control. The underlying metrically consistent environment model also introduces the capacity to remove latency and enhance video compression. Display quality is sufficiently high that the user experience is similar to a driving video game where the surfaces used are textured with live video.

Fast feature detection and stochastic parameter estimation of road shape using multiple LIDAR

This paper describes an algorithm for an autonomous car to identify the shape of a roadway by detecting geometric features via LIDAR. The data from multiple LIDAR are fused together to detect both obstacles as well as geometric features such as curbs, berms, and shoulders. These features identify the boundaries of the roadway and are used by a stochastic state estimator to identify the most likely road shape. This algorithm has been used successfully to allow an autonomous car to drive on paved roadways as well as on off-road trails without requiring different sets of parameters for the different domains.

Real-time photo-realistic visualization of 3D environments for enhanced tele-operation of vehicles

This paper describes a method for creating photorealistic three-dimensional (3D) models of real-world environments in real-time for the purpose of improving and extending the capabilities of vehicle tele-operation. Our approach utilizes the combined data from a laser scanner (for modeling 3D geometry) and a video camera (for modeling surface appearance). The sensors are mounted on a moving vehicle platform, and a photo-realistic 3D model of the vehicles environment is generated and displayed to the remote operator in real time. Our model consists of three main components: a textured ground surface, textured or colorized non-ground objects, and a textured background for representing regions beyond the laser scanners sensing horizon. Our approach enables many unique capabilities for vehicle tele-operation, including viewing the scene from virtual viewpoints (e.g., behind the vehicle or top down), seamless augmentation of the environment with digital objects, and improved robustness to transmission latencies and data dropouts.

Software Infrastructure for an Autonomous Ground Vehicle

The DARPA Urban Challenge required robots to drive 60 miles on suburban roads while following the rules of the road in interactions with human drivers and other robots. Tartan Racing’s Boss won the competition, completing the course in just over 4 hours. This paper describes the software infrastructure developed by the team to support the perception, planning, behavior generation, and other artificial intelligence components of Boss. We discuss the organizing principles of the infrastructure, as well as details of the operator interface, interprocess communications, data logging, system configuration, process management, and task framework, with attention to the requirements that led to the design. We identify the requirements as valuable re-usable artifacts of the development process.