Visual Navigation for Mobile Robots
Nils Andersen,Jens Christian Andersen,Enis Bayramoglu,Ole Ravn +3 more
- 01 Mar 2010
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Citations
Object-oriented landmark recognition for UAV-navigation
TL;DR: A landmark recognition system looking for salient man-made infrastructure and a system for adapting a system, designed for a central European country (Germany), for use in a more Eastern region (Turkey).
Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure
Simge Nur Aslan,Ayşegül Uçar,Cuneyt Guzelis +2 more
- 01 Aug 2020
TL;DR: This study aims for Robotis-Op3 humanoid robot to recognize the different shaped objects with deep learning methods and proposes new models with small structure of Convolutional Neural Networks (CNNs) that produced higher performance than complex models.
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Research of smart real-time robot navigation system
Budi Rahmani,Agus Harjoko,Tri Kuntoro Priyambodo,Hugo Aprilianto +3 more
- 11 Feb 2016
TL;DR: The result shows that the humanoid robot measures its distance to the orange ball on green floor can be used as one of smart navigation system using a camera as the only one sensor to perceive the information of environtment.
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An Improved Particle Filter SLAM Algorithm for AGVs
Qi-Ming Chen,Dong Chaoyi,Ying-Ze Mu,Bo-Chen Li,Zhi-Qing Fan,Wang Qilai +5 more
- 17 Jul 2020
TL;DR: The experimental results show that the traditional algorithm is significantly improved in reducing computational complexity, improving positioning accuracy and robustness, etc, and has a more significant positioning and mapping effects, compared with the traditional RBPF-SLAM algorithm.
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PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization
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- 15 Sep 2022
TL;DR: A novel probability map based on the Gaussian distribution assumption is proposed that transforms the semantic binary object detection into probability results, which help establish a probabilistic data association between artificial features and semantic info.
References
A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
29.9K
Use of the Hough transformation to detect lines and curves in pictures
Richard O. Duda,Peter E. Hart +1 more
TL;DR: It is pointed out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further, and how the method can be used for more general curve fitting.
Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
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.
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara Nefian,Pamela Mahoney +29 more
TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high-speed desert driving without manual intervention using state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning.
1.5K
Optimization of the simultaneous localization and map-building algorithm for real-time implementation
Jose Guivant,Eduardo Nebot +1 more
- 01 Jun 2001
TL;DR: Addresses real-time implementation of the simultaneous localization and map-building (SLAM) algorithm and presents optimal algorithms that consider the special form of the matrices and a new compressed filler that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors.
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