Multi-sensor Appearance-Based Place Recognition

This paper describes a multi-sensor appearance-based place recognition system suitable for robotic mapping. Unlike systems that extract features from visual imagery only, here we apply the well known Bag-of-Words approach to features extracted from both visual and range sensors. By applying this tec...

Full description

Saved in:
Bibliographic Details
Main Authors: Collier, Jack, Se, Stephen, Kotamraju, Vinay
Format: Conference Proceeding
Language:English
Subjects:
Appearance-Based
Feature extraction
Laser radar
Lighting
place recognition
Sensors
SIFT
SLAM
Transforms
VD-LSD
Visualization
Vocabulary
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper describes a multi-sensor appearance-based place recognition system suitable for robotic mapping. Unlike systems that extract features from visual imagery only, here we apply the well known Bag-of-Words approach to features extracted from both visual and range sensors. By applying this technique to both sensor streams simultaneously we can overcome the deficiencies of each individual sensor. We show that LIDAR-based place recognition using a generative model learnt from Variable Dimensional Local Shape Descriptors can be used to perform place recognition regardless of lighting conditions or large changes in orientation, including traversing loops backward. Likewise, we are still able to exploit the feature rich place recognition that visual systems provide. Using a pose verification system we are able to effectively discard false positive loop detections. We present experimental results that highlight the strength of our approach and investigate alternative techniques for combining the results from the individual sensor streams. The multi-sensor approach enables the two sensors to complement each other well in large urban and rural environments under variable lighting conditions.
DOI:10.1109/CRV.2013.35