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Book ChapterDOI

A Trail-Following Robot Which Uses Appearance and Structural Cues

Christopher Rasmussen, +2 more
- pp 265-279
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TLDR
A wheeled robotic system which navigates along outdoor “trails” intended for hikers and bikers through a combination of appearance and structural cues derived from stereo omnidirectional color cameras and a tiltable laser range-finder, which is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions.
Abstract: 
We describe a wheeled robotic system which navigates along outdoor “trails” intended for hikers and bikers. Through a combination of appearance and structural cues derived from stereo omnidirectional color cameras and a tiltable laser range-finder, the system is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions. The approaching trail region is efficiently segmented in a top-down fashion based on color, brightness, and/or height contrast with flanking areas, and a differential motion planner searches for maximally-safe paths within that region according to several criteria. When the trail tracker’s confidence drops the robot slows down to allow a more detailed search, and when it senses a dangerous situation due to excessive slope, dense trailside obstacles, or visual trail segmentation failure, it stops entirely to acquire and analyze a ladar-derived point cloud in order to reset the tracker. Our system’s ability to negotiate a variety of challenging trail types over long distances is demonstrated through a number of live runs through different terrain and in different weather conditions.

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Citations
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Journal ArticleDOI

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Alessandro Giusti, +11 more
TL;DR: This work proposes a different approach to perceive forest trials based on a deep neural network used as a supervised image classifier that outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task.
Proceedings ArticleDOI

Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness

Nikolai Smolyanskiy, +3 more
TL;DR: A micro aerial vehicle (MAV) system, built with inexpensive off-the-shelf hardware, for autonomously following trails in unstructured, outdoor environments such as forests, introduces a deep neural network called TrailNet for estimating the view orientation and lateral offset of the MAV with respect to the trail center.
Journal ArticleDOI

Lighting-invariant Adaptive Route Following Using Iterative Closest Point Matching

Philipp Krusi, +5 more
- 01 Jun 2015 - 
TL;DR: This work presents a T&R system based on iterative closest point matching (ICP) using data from a spinning three‐dimensional (3D) laser scanner that is highly accurate, robust to dynamic scenes and extreme changes in the environment, and independent of ambient lighting.
Proceedings ArticleDOI

Deep Trail-Following Robotic Guide Dog in Pedestrian Environments for People who are Blind and Visually Impaired - Learning from Virtual and Real Worlds

Tzu-Kuan Chuang, +9 more
TL;DR: This work proposed an autonomous, trail-following robotic guide dog that would be robust to variances of background textures, illuminations, and interclass trail variations and a deep convolutional neural network is trained from both the virtual and realworld environments.
Posted Content

Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness

Nikolai Smolyanskiy, +3 more
- 07 May 2017 - 
TL;DR: In this article, a micro aerial vehicle (MAV) is used for autonomously following trails in unstructured, outdoor environments such as forests, using a deep neural network (DNN) called TrailNet for estimating the view orientation and lateral offset of the MAV.
References
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MonographDOI

Planning Algorithms: Introductory Material

Steven M. LaValle
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Book

Planning Algorithms

Steven M. LaValle
Journal ArticleDOI

Stereo Processing by Semiglobal Matching and Mutual Information

Heiko Hirschmüller
- 01 Feb 2008 - 
TL;DR: This paper describes the Semi-Global Matching (SGM) stereo method, which uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images and demonstrates a tolerance against a wide range of radiometric transformations.
Journal ArticleDOI

Stanley: The Robot that Won the DARPA Grand Challenge

Sebastian Thrun, +29 more
- 01 Sep 2006 - 
TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.
Proceedings ArticleDOI

A metric for distributions with applications to image databases

Yossi Rubner, +2 more
TL;DR: This paper uses the Earth Mover's Distance to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays, and proposes a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.