NASVIO

This repository contains the evaluation code for Search for efficient deep visual-inertial odometry through neural architecture search and the searched models

Data Preparation

The test dataset is KITTI Odometry dataset. The IMU data after pre-processing is provided under data/imus. To download the images and poses, please run

  $cd data
  $source data_prep.sh 

Pretrained checkpoints on searched best models

Two checkpoints with low FLOPS target (flops_target.zip) and low latency target (latency_target.zip) are provided. Simply unzip to retrieve the checkpoints.

Test the pretrained models

Select which model to run by changing the self.target parameter (flops or latency) in the params.py. Then run:

  python3 test.py