22,691 research outputs found
Pose-based slam with probabilistic scan matching algorithm using a mechanical scanned imaging sonar
This paper proposes a pose-based algorithm to solve the full SLAM problem
for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and
possibly unstructured environment. The technique incorporate probabilistic scan
matching with range scans gathered from a Mechanical Scanned Imaging Sonar
(MSIS) and the robot dead-reckoning displacements estimated from a Doppler Velocity
Log (DVL) and a Motion Reference Unit (MRU). The raw data from the sensors
are processed and fused in-line. No priory structural information or initial pose are
considered. The algorithm has been tested on an AUV guided along a 600m path
within a marina environment, showing the viability of the proposed approach.Peer Reviewe
Learning for Ground Robot Navigation with Autonomous Data Collection
Robot navigation using vision is a classic example of a scene understanding problem. We describe a novel approach to estimating the traversability of an unknown environment based on modern object recognition methods. Traversability is an example of an affordance jointly determined by the environment and the physical characteristics of a robot vehicle, whose definition is clear in context. However, it is extremely difficult to estimate the traversability of a given terrain structure in general, or to find rules which work for a wide variety of terrain types. However, by learning to recognize similar terrain structures, it is possible to leverage a limited amount of interaction between the robot and its environment into global statements about the traversability of the scene. We describe a novel on-line learning algorithm that learns to recognize terrain features from images and aggregate the traversability information acquired by a navigating robot. An important property of our method, which is desirable for any learning-based approach to object recognition, is the ability to autonomously acquire arbitrary amounts of training data as needed without any human intervention. Tests of our algorithm on a real robot in complicated unknown natural environments suggest that it is both robust and efficient
DeepNav: Learning to Navigate Large Cities
We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for
navigating large cities using locally visible street-view images. The DeepNav
agent learns to reach its destination quickly by making the correct navigation
decisions at intersections. We collect a large-scale dataset of street-view
images organized in a graph where nodes are connected by roads. This dataset
contains 10 city graphs and more than 1 million street-view images. We propose
3 supervised learning approaches for the navigation task and show how A* search
in the city graph can be used to generate supervision for the learning. Our
annotation process is fully automated using publicly available mapping services
and requires no human input. We evaluate the proposed DeepNav models on 4
held-out cities for navigating to 5 different types of destinations. Our
algorithms outperform previous work that uses hand-crafted features and Support
Vector Regression (SVR)[19].Comment: CVPR 2017 camera ready versio
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