eacsai / boosting3dofaccuracy

ICCV2023: Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer

Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer, ICCV 2023

Abstract

Image retrieval-based cross-view localization methods often lead to very coarse camera pose estimation, due to the limited sampling density of the database satellite images. In this paper, we propose a method to increase the accuracy of a ground camera's location and orientation by estimating the relative rotation and translation between the ground-level image and its matched/retrieved satellite image. Our approach designs a geometry-guided cross-view transformer that combines the benefits of conventional geometry and learnable cross-view transformers to map the ground-view observations to an overhead view. Given the synthesized overhead view and observed satellite feature maps, we construct a neural pose optimizer with strong global information embedding ability to estimate the relative rotation between them. After aligning their rotations, we develop an uncertainty-guided spatial correlation to generate a probability map of the vehicle locations, from which the relative translation can be determined. Experimental results demonstrate that our method significantly outperforms the state-of-the-art. Notably, the likelihood of restricting the vehicle lateral pose to be within 1m of its Ground Truth (GT) value on the cross-view KITTI dataset has been improved from $35.54%$ to $76.44%$, and the likelihood of restricting the vehicle orientation to be within $1^{\circ}$ of its GT value has been improved from $19.64%$ to $99.10%$.

Experiment Dataset

We use three existing dataset to do the experiments: KITTI, Ford-AV and Oxford RobotCar. For our collected satellite images for KITTI and Ford-AV, please first fill this Google Form, we will then send you the link for download.

• KITTI: Please first download the raw data (ground images) from http://www.cvlibs.net/datasets/kitti/raw_data.php, and store them according to different date (not category). Your dataset folder structure should be like:

KITTI:

raw_data:

2011_09_26:

  2011_09_26_drive_0001_sync:
  
    image_00:

image_01:

image_02:

image_03:

oxts:

  ...
  
2011_09_28:

2011_09_29:

2011_09_30:

2011_10_03:

satmap:

2011_09_26:

2011_09_29:

2011_09_30:

2011_10_03:

• Ford-AV: The ground images and camera calibration files can be accessed from https://avdata.ford.com/downloads/default.aspx. Please follow their original structure to save them on your computer. For the satellite images, please put them under their corresponding log folder. Here is an example:

Ford:

2017-08-04:

V2:

  Log1:
  
    2017-08-04-V2-Log1-FL

    SatelliteMaps_18:

    grd_sat_quaternion_latlon.txt

    grd_sat_quaternion_latlon_test.txt

2017-10-26:

Calibration-V2:

• For the Cross-view Oxford RobotCar dataset, please refer to this github page: https://github.com/tudelft-iv/CrossViewMetricLocalization.git.
• For the VIGOR dataset, please refer to the following two github pages: https://github.com/Jeff-Zilence/VIGOR.git https://github.com/tudelft-iv/SliceMatch.git

Codes

1. Training on 2DoF(only location) pose estimation:

   python train_kitti_2DoF.py --batch_size 1

   python train_ford_2DoF.py --batch_size 1 --train_log_start 0 --train_log_end 1

   python train_ford_2DoF.py --batch_size 1 --train_log_start 1 --train_log_end 2

   python train_ford_2DoF.py --batch_size 1 --train_log_start 2 --train_log_end 3

   python train_ford_2DoF.py --batch_size 1 --train_log_start 3 --train_log_end 4

   python train_ford_2DoF.py --batch_size 1 --train_log_start 4 --train_log_end 5

   python train_ford_2DoF.py --batch_size 1 --train_log_start 5 --train_log_end 6

   python train_oxford_2DoF.py --batch_size 1

   python train_VIGOR_2DoF.py --area cross

   python train_VIGOR_2DoF.py --area same

2. Training on 3DoF (joint location and translation) pose estimation:

   python train_kitti_3DoF.py --batch_size 1

   python train_ford_3DoF.py --batch_size 1 --train_log_start 0 --train_log_end 1

   python train_ford_3DoF.py --batch_size 1 --train_log_start 1 --train_log_end 2

   python train_ford_3DoF.py --batch_size 1 --train_log_start 2 --train_log_end 3

   python train_ford_3DoF.py --batch_size 1 --train_log_start 3 --train_log_end 4

   python train_ford_3DoF.py --batch_size 1 --train_log_start 4 --train_log_end 5

   python train_ford_3DoF.py --batch_size 1 --train_log_start 5 --train_log_end 6

3. Evaluation:

   Plz simply add "--test 1" after the training commands. E.g.

   python train_kitti_3DoF.py --batch_size 1 --test 1

You are free to change batch size according to your own GPU memory.

Models:

Our trained models are available here.

Publications

This work is submitted to ICCV 2023.