Charles E. Thorpe

Vision and navigation for the Carnegie-Mellon Navlab

A distributed architecture articulated around the CODGER (communication database with geometric reasoning) knowledge database is described for a mobile robot system that includes both perception and navigation tools. Results are described for vision and navigation tests using a mobile testbed that integrates perception and navigation capabilities that are based on two types of vision algorithms: color vision for road following, and 3-D vision for obstacle detection and avoidance. The perception modules are integrated into a system that allows the vehicle to drive continuously in an actual outdoor environment. The resulting system is able to navigate continuously on roads while avoiding obstacles. >

Simultaneous localization, mapping and moving object tracking

Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves both simultaneous localization and mapping (SLAM) in dynamic environments and detecting and tracking these dynamic objects. In this paper, a mathematical framework is established to integrate SLAM and moving object tracking. Two solutions are described: SLAM with generalized objects, and SLAM with detection and tracking of moving objects (DATMO). SLAM with generalized objects calculates a joint posterior over all generalized objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modeling of generalized objects. Unfortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes the estimation problem into two separate estimators. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional than SLAM with generalized objects. Both SLAM and moving object tracking from a moving vehicle in crowded urban areas are daunting tasks. Based on the SLAM with DATMO framework, practical algorithms are proposed which deal with issues of perception modeling, data association, and moving object detection. The implementation of SLAM with DATMO was demonstrated using data collected from the CMU Navlab11 vehicle at high speeds in crowded urban environments. Ample experimental results shows the feasibility of the proposed theory and algorithms.

Vehicle Teleoperation Interfaces

Despite advances in autonomy, there will always be a need for human involvement in vehicle teleoperation. In particular, tasks such as exploration, reconnaissance and surveillance will continue to require human supervision, if not guidance and direct control. Thus, it is critical that the operator interface be as efficient and as capable as possible. In this paper, we provide an overview of vehicle teleoperation and present a summary of interfaces currently in use.

Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas

The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.

Integrated path planning and dynamic steering control for autonomous vehicles

A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals. Simulation results are presented to demonstrate the control scheme.

SCARF: a color vision system that tracks roads and intersections

SCARF, a color vision system that recognizes difficult roads and intersections, is presented. It has been integrated into several navigation systems that drive a robot vehicle, the Navlab, on a variety of roads in many different weather conditions. SCARF recognizes roads that have degraded surfaces and edges with no lane markings in difficult shadow conditions. It also recognizes intersections with or without predictions from the navigation system. This is the first system that detects intersections in images without a priori knowledge of the intersection shape and location. SCARF uses Bayesian classification to determine a road-surface likelihood for each pixel in a reduced color image. It then evaluates a number of road and intersection candidates by matching an ideal road-surface likelihood image with the results from the Bayesian classification. The best matching candidate is passed to a path-planning system that navigates the robot vehicle on the road or intersection. The SCARF system is described in detail, results on a variety of images are presented, and Navlab test runs using SCARF are discussed. >

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.

Path Relaxation: Path Planning for a Mobile Robot

Path Relaxation is a method of planning safe paths around obstacles for mobile robots. It works in two steps: a global grid search that finds a rough path, followed by a local relaxation step that adjusts each node on the path to lower the overall path cost. The representation used by Path Relaxation allows an explicit tradeoff among length of path, clearance away from obstacles, and distance traveled through unmapped areas.

UNSCARF-a color vision system for the detection of unstructured roads

The problem of navigating a robot vehicle on unstructured roads that have no lane markings, may have degraded surfaces and edges, and may be partially obscured by strong shadows is addressed. These conditions cause many road following systems to fail. The authors have build a system, UNSCARF, which is based on pattern recognition techniques, for successfully navigating on a variety of unstructured roads. UNSCARF does not need a road location prediction to find the location of the road; therefore, UNSCARF can be used as a bootstrapping system. It uses a clustering technique to group pixels with similar colors and locations. It then matches models of road shape to locate the roads in the image. These methods are more robust in noisy conditions than other road interpretation techniques. UNSCARF has been integrated into a navigation system that has successfully driven a test vehicle in may types of weather conditions.<<ETX>>

Robot, Asker of Questions

Collaborative control is a teleoperation system model based on human–robot dialogue. With this model, the robot asks questions to the human in order to obtain assistance with cognition and perception. This enables the human to function as a resource for the robot and help to compensate for limitations of autonomy. To understand how collaborative control influences human–robot interaction, we performed a user study based on contextual inquiry (CI). The study revealed that: (1) dialogue helps users understand problems encountered by the robot and (2) human assistance is a limited resource that must be carefully managed.