Neural Network Perception for Mobile Robot Guidance
Neural Network Perception for Mobile Robot Guidance
Learning View Graphs for Robot Navigation
Autonomous Robots - Special issue on autonomous agents
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the Agility of Keyframe-Based SLAM
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers
International Journal of Robotics Research
Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models
Persistent Navigation and Mapping using a Biologically Inspired SLAM System
International Journal of Robotics Research
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory
International Journal of Robotics Research
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
C2TAM: A Cloud framework for cooperative tracking and mapping
Robotics and Autonomous Systems
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In this paper we use the algorithm SeqSLAM to address the question, how little and what quality of visual information is needed to localize along a familiar route? We conduct a comprehensive investigation of place recognition performance on seven datasets while varying image resolution (primarily 1 to 512 pixel images), pixel bit depth, field of view, motion blur, image compression and matching sequence length. Results confirm that place recognition using single images or short image sequences is poor, but improves to match or exceed current benchmarks as the matching sequence length increases. We then present place recognition results from two experiments where low-quality imagery is directly caused by sensor limitations; in one, place recognition is achieved along an unlit mountain road by using noisy, long-exposure blurred images, and in the other, two single pixel light sensors are used to localize in an indoor environment. We also show failure modes caused by pose variance and sequence aliasing, and discuss ways in which they may be overcome. By showing how place recognition along a route is feasible even with severely degraded image sequences, we hope to provoke a re-examination of how we develop and test future localization and mapping systems.