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This paper presents a method of road detection and mapping using accumulated 3D data from 2D scans. The idea of 3D rolling window is introduced, and its probabilisitc characteristics are studied. A cascaded road detection process is developed with region-growing and classification methods. A probabilistic framework is utlized for road mapping purposes with the detection results. The performance of...
This paper presents a precise localization algorithm for vehicles in 3D urban environment with only one 2D LIDAR and odometry information. A novel idea of synthetic 2D LIDAR is proposed to solve the localization problem on a virtual 2D plane. A Monte Carlo Localization scheme is adopted for vehicle position estimation, based on synthetic LIDAR measurements and odometry information. The accuracy and...
In this paper we describe an autonomous vehicle that aims at providing shared transportation services in a mobility on demand context. As the service is limited to a known urban environment, prior knowledge of the environment can be exploited, as well as existing infrastructure sensors such as security cameras. We argue that utilizing infrastructure sensors yields greater safety of operation and allows...
We present an autonomous vehicle providing mobility-on-demand service in a crowded urban environment. The focus in developing the vehicle has been to attain autonomous driving with minimal sensing and low cost, off-the-shelf sensors to ensure the system's economic viability. The autonomous vehicle has successfully completed over 50 km handling numerous mobility requests during the course of multiple...
One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicle's localization. This paper introduces...
This paper addresses the problem of autonomous manipulation of a priori unknown palletized cargo with a robotic lift truck (forklift). Specifically, we describe coupled perception and control algorithms that enable the vehicle to engage and place loaded pallets relative to locations on the ground or truck beds. Having little prior knowledge of the objects with which the vehicle is to interact, we...
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