SE3 Pose Interp - Interpolate camera pose or trajectory in SE3, pose interpolation, trajectory interpolation

Overview

SE3 Pose Interpolation

Pose estimated from SLAM system are always discrete, and often not equal to the original sequence frame size.

This repo helps to remedy it and interpolate the pose for any interval timestamp you want.

p_interp_demo

Dependencies & Environment

The repo has minimal requirement:

python==3.7
numpy==1.19
transformations==2021.6.6
evo==v1.13.5

How to Run

The script takes two files as input data, keyframe pose and lookup timestamps, the lookup timestamps contains much more timestamps data than keyframe sequences.

To run this script simply try:

python pose_interp.py --kf_pose ./data/kf_pose_result_tum.txt \
                      --timestamps ./data/timestamps.txt

The output file will be saved at the same directory with extra suffix _interp.txt

File format

Please make sure the estimated key-frame pose file (e.g.: ./data/kf_pose_result_tum.txt) is in TUM format:

timestamp t_x t_y t_z q_x q_y q_z q_w

The timestamps file for all frames (e.g.: ./data/timestamps.txt) is saved as following:

sequence_id timestamp

The output interpolated pose file which contains pose for each timestamp of every frame in the original sequence (e.g.: ./data/kf_pose_result_tum_interp.txt) is also in TUM format:

timestamp t_x t_y t_z q_x q_y q_z q_w

Visualization

We use evo to visualize the pose file, simply run the following code to get the plots

pose_interp

To run the visualization code, please try:

python pose_vis.py --kf_pose ./data/kf_pose_result_tum_vis.txt --full_pose ./data/kf_pose_result_tum_interp.txt

Please note that file kf_pose_result_tum_vis.txt is downsampled from original keyframe sequence kf_pose_result_tum_vis.txt for better visualization effect.

Disclaimer

This repo is adapted from https://github.com/ethz-asl/robotcar_tools/blob/master/python/interpolate_poses.py

The modification includes:

  • fixed axis align mis-match bug
  • add visualization for sanity check
  • consistent data format with clear comments
  • loop up any given interval timestamp

If you use part of this code please cite:

@software{cheng2022poseinterp,
  author = {Lisa, Mona and Bot, Hew},
  doi = {10.5281/zenodo.1234},
  month = {12},
  title = {{SE3 Pose Interpolation Toolbox}},
  url = {https://github.com/rancheng/se3_pose_interp},
  version = {1.0.0},
  year = {2022}
}

and

@article{RobotCarDatasetIJRR,
  Author = {Will Maddern and Geoff Pascoe and Chris Linegar and Paul Newman},
  Title = {{1 Year, 1000km: The Oxford RobotCar Dataset}},
  Journal = {The International Journal of Robotics Research (IJRR)},
  Volume = {36},
  Number = {1},
  Pages = {3-15},
  Year = {2017},
  doi = {10.1177/0278364916679498},
  URL =
{http://dx.doi.org/10.1177/0278364916679498},
  eprint =
{http://ijr.sagepub.com/content/early/2016/11/28/0278364916679498.full.pdf+html},
  Pdf = {http://robotcar-dataset.robots.ox.ac.uk/images/robotcar_ijrr.pdf}}

License

SE3_Pose_Interp is released under a MIT license (see LICENSE.txt)

If you use SE3_Pose_Interp in an academic work, please cite the most relevant publication associated by visiting: https://rancheng.github.io

Owner
Ran Cheng
Robotics, Vision, Learning
Ran Cheng
YuNetのPythonでのONNX、TensorFlow-Lite推論サンプル

YuNet-ONNX-TFLite-Sample YuNetのPythonでのONNX、TensorFlow-Lite推論サンプルです。 TensorFlow-LiteモデルはPINTO0309/PINTO_model_zoo/144_YuNetのものを使用しています。 Requirement Op

KazuhitoTakahashi 8 Nov 17, 2021
Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Online-compatible Unsupervised Non-resonant Anomaly Detection Repository Repository containing all scripts used in the studies of Online-compatible Un

0 Nov 09, 2021
This repository lets you interact with Lean through a REPL.

lean-gym This repository lets you interact with Lean through a REPL. See Formal Mathematics Statement Curriculum Learning for a presentation of lean-g

OpenAI 87 Dec 28, 2022
Variational autoencoder for anime face reconstruction

VAE animeface Variational autoencoder for anime face reconstruction Introduction This repository is an exploratory example to train a variational auto

Minzhe Zhang 2 Dec 11, 2021
Steerable discovery of neural audio effects

Steerable discovery of neural audio effects Christian J. Steinmetz and Joshua D. Reiss Abstract Applications of deep learning for audio effects often

Christian J. Steinmetz 182 Dec 29, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
Implementation of UNET architecture for Image Segmentation.

Semantic Segmentation using UNET This is the implementation of UNET on Carvana Image Masking Kaggle Challenge About the Dataset This dataset contains

Anushka agarwal 4 Dec 21, 2021
Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

CaloGAN Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. This repository c

Deep Learning for HEP 101 Nov 13, 2022
[IROS2021] NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences

NYU-VPR This repository provides the experiment code for the paper Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymiza

Automation and Intelligence for Civil Engineering (AI4CE) Lab @ NYU 22 Sep 28, 2022
Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Introduction This repository contains research code for the ACL 2021 paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual

AdapterHub 20 Aug 04, 2022
BBScan py3 - BBScan py3 With Python

BBScan_py3 This repository is forked from lijiejie/BBScan 1.5. I migrated the fo

baiyunfei 12 Dec 30, 2022
Merlion: A Machine Learning Framework for Time Series Intelligence

Merlion: A Machine Learning Library for Time Series Table of Contents Introduction Installation Documentation Getting Started Anomaly Detection Foreca

Salesforce 2.8k Dec 30, 2022
Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022
A implemetation of the LRCN in mxnet

A implemetation of the LRCN in mxnet ##Abstract LRCN is a combination of CNN and RNN ##Installation Download UCF101 dataset ./avi2jpg.sh to split the

44 Aug 25, 2022
Group Activity Recognition with Clustered Spatial Temporal Transformer

GroupFormer Group Activity Recognition with Clustered Spatial-TemporalTransformer Backbone Style Action Acc Activity Acc Config Download Inv3+flow+pos

28 Dec 12, 2022
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

NeRF-pytorch NeRF (Neural Radiance Fields) is a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Here are

Yen-Chen Lin 3.2k Jan 08, 2023
A multi-mode modulator for multi-domain few-shot classification (ICCV)

A multi-mode modulator for multi-domain few-shot classification (ICCV)

Yanbin Liu 8 Apr 28, 2022
Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

5 Nov 21, 2022
Automatically align face images 🙃→🙂. Can also do windowing and warping.

Automatic Face Alignment (AFA) Carl M. Gaspar & Oliver G.B. Garrod You have lots of photos of faces like this: But you want to line up all of the face

Carl Michael Gaspar 15 Dec 12, 2022
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022