Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching

08/21/2020
by   Zexi Chen, et al.
2

The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this paper, we present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. In DPCN, the primary component is a differentiable correlation-based estimator that back-propagates the pose error to learnable feature extractors, which addresses the problem that there are no direct common features for supervision. Also, it eliminates the exhaustive evaluation in some previous methods, improving efficiency. With the interpretable modeling, the network is light-weighted and promising for better generalization. We evaluate the system on both the simulation data and Aero-Ground Dataset which consists of heterogeneous sensor images and aerial images acquired by satellites or aerial robots. The results show that our method is able to match the heterogeneous sensor measurements, outperforming the comparative traditional phase correlation and other learning-based methods.

READ FULL TEXT

page 2

page 4

page 5

page 7

page 8

page 13

page 14

research
03/01/2021

Collaborative Recognition of Feasible region with Aerial and Ground Robots through DPCN

Ground robots always get collision in that only if they get close to the...
research
06/12/2022

DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration

Pose registration is critical in vision and robotics. This paper focuses...
research
07/11/2019

Optimal Feature Transport for Cross-View Image Geo-Localization

This paper addresses the problem of cross-view image based localization,...
research
05/15/2023

CMSG Cross-Media Semantic-Graph Feature Matching Algorithm for Autonomous Vehicle Relocalization

Relocalization is the basis of map-based localization algorithms. Camera...
research
07/04/2020

Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges and Opportunities

The objective of this paper is to present a systematic review of existin...
research
03/30/2021

Physics-based Differentiable Depth Sensor Simulation

Gradient-based algorithms are crucial to modern computer-vision and grap...
research
06/14/2023

3-Dimensional Sonic Phase-invariant Echo Localization

Parallax and Time-of-Flight (ToF) are often regarded as complementary in...

Please sign up or login with your details

Forgot password? Click here to reset