Showing papers by "Paul Newman published in 2006"

Using laser range data for 3D SLAM in outdoor environments

Abstract: Traditional simultaneous localization and mapping (SLAM) algorithms have been used to great effect in flat, indoor environments such as corridors and offices. We demonstrate that with a few augmentations, existing 2D SLAM technology can be extended to perform full 3D SLAM in less benign, outdoor, undulating environments. In particular, we use data acquired with a 3D laser range finder. We use a simple segmentation algorithm to separate the data stream into distinct point clouds, each referenced to a vehicle position. The SLAM technique we then adopt inherits much from 2D delayed state (or scan-matching) SLAM in that the state vector is an ever growing stack of past vehicle positions and inter-scan registrations are used to form measurements between them. The registration algorithm used is a novel combination of previous techniques carefully balancing the need for maximally wide convergence basins, robustness and speed. In addition, we introduce a novel post-registration classification technique to detect matches which have converged to incorrect local minima

Outdoor SLAM using visual appearance and laser ranging

Abstract: This paper describes a 3D SLAM system using information from an actuated laser scanner and camera installed on a mobile robot. The laser samples the local geometry of the environment and is used to incrementally build a 3D point-cloud map of the workspace. Sequences of images from the camera are used to detect loop closure events (without reference to the internal estimates of vehicle location) using a novel appearance-based retrieval system. The loop closure detection is robust to repetitive visual structure and provides a probabilistic measure of confidence. The images suggesting loop closure are then further processed with their corresponding local laser scans to yield putative Euclidean image-image transformations. We show how naive application of this transformation to effect the loop closure can lead to catastrophic linearization errors and go on to describe a way in which gross, pre-loop closing errors can be successfully annulled. We demonstrate our system working in a challenging, outdoor setting containing substantial loops and beguiling, gently curving traversals. The results are overlaid on an aerial image to provide a ground truth comparison with the estimated map. The paper concludes with an extension into the multi-robot domain in which 3D maps resulting from distinct SLAM sessions (no common reference frame) are combined without recourse to mutual observation

A method for protocol‐based collision avoidance between autonomous marine surface craft

Abstract: This paper is concerned with the in-field autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the Coast Guard Collision Regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture, the flexibility exploited by humans, this work applies a novel method of multiobjective optimization, interval programming, in a behavior-based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the first in-field demonstration of multiobjective optimization applied to autonomous COLREGS-based marine vehicle navigation. © 2006 Wiley Periodicals, Inc.

Navigation of unmanned marine vehicles in accordance with the rules of the road

Abstract: This paper is concerned with the in-field autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the coast guard collision regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture the flexibility exploited by humans, this work applies a novel method of multi-objective optimization, interval programming, in a behavior-based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the first in-field demonstration of multiobjective optimization applied to autonomous COLREGS-based marine vehicle navigation

Multi-objective optimization of sensor quality with efficient marine vehicle task execution

Abstract: This paper describes the in-field operation of two interacting autonomous marine vehicles to demonstrate the suitability of interval programming (IvP), a novel mathematical model for multiple-objective optimization. Broadly speaking, IvP coordinates competing control needs such as primary task execution that depends on a sufficient position estimate, and vehicle maneuvers that will improve that position estimate. In this work, vehicles cooperate to improve their position estimates using a sequence of vehicle-to-vehicle range estimates from acoustic modems. Coordinating primary task execution and sensor quality maintenance is a ubiquitous problem, especially in underwater marine vehicles. This work represents the first use of multiobjective optimization in a behavior-based architecture to address this problem

Motion Estimation from Map Quality with Millimeter Wave Radar

Abstract: Simultaneous localization and mapping (SLAM) builds maps of a priori unknown environments. Whilst this key mobile robotic competency continues to receive substantial attention, less attention has been paid to assessing the quality of the resulting maps. This paper proposes a way to quantify the intrinsic quality of point-cloud maps built from a stream of range bearing measurements. It does so by considering both the temporal and spatial distribution of the points within the map. One of the causes of unsatisfactory maps is the execution of unmodelled or poorly sensed vehicle manoeuvres. In this paper we show that by maximizing the quality of the map as a function of a motion parameterization, the vehicle motion can be recovered while correcting the map at the same time. In contrast to typical scan matching techniques, we do not rely on segmentation of the measurement stream into two separate "scans"; Instead we treat the measurement sequence as a continuous signal. We illustrate the efficacy of this approach by processing range data from a 77 GHz millimeter wave radar that completes 2 rotations per second. We show that despite this acquisition speed being commensurate with vehicle rotation rates, we are able to extract the underlying vehicle motion and yield crisp, well aligned point clouds

Trajectory Sonar Perception in the Ligurian Sea

Abstract: : This paper describes a new technique for tracking locally curved unknown objects using sonar. The approach explicitly accounts for relevant robot dynamics. Objects are tracked by looking for temporal sequences of observations that fit a kinematic model. The method is illustrated using data from a synthetic aperture sonar gathered by a BPAUV at NATO SACLANT's GOATS 2002 experiment in the Ligurian Sea.