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
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Multipath RefineNet A MATLAB based framework for semantic image segmentation and general dense prediction tasks on images. This is the source code for

Guosheng Lin 575 Dec 06, 2022
Neuron Merging: Compensating for Pruned Neurons (NeurIPS 2020)

Neuron Merging: Compensating for Pruned Neurons Pytorch implementation of Neuron Merging: Compensating for Pruned Neurons, accepted at 34th Conference

Woojeong Kim 33 Dec 30, 2022
Transformer part of 12th place solution in Riiid! Answer Correctness Prediction

kaggle_riiid Transformer part of 12th place solution in Riiid! Answer Correctness Prediction. Please see here for more information. Execution You need

Sakami Kosuke 2 Apr 23, 2022
git《Commonsense Knowledge Base Completion with Structural and Semantic Context》(AAAI 2020) GitHub: [fig1]

Commonsense Knowledge Base Completion with Structural and Semantic Context Code for the paper Commonsense Knowledge Base Completion with Structural an

AI2 96 Nov 05, 2022
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 02, 2023
Simulating an AI playing 2048 using the Expectimax algorithm

2048-expectimax Simulating an AI playing 2048 using the Expectimax algorithm The base game engine uses code from here. The AI player is modeled as a m

Subha Ramesh 2 Jan 31, 2022
Voice control for Garry's Mod

WIP: Talonvoice GMod integrations Very work in progress voice control demo for Garry's Mod. HOWTO Install https://talonvoice.com/ Press https://i.imgu

Meta Construct 5 Nov 15, 2022
pixelNeRF: Neural Radiance Fields from One or Few Images

pixelNeRF: Neural Radiance Fields from One or Few Images Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa UC Berkeley arXiv: http://arxiv.org/abs/2

Alex Yu 1k Jan 04, 2023
Official Repository for our ECCV2020 paper: Imbalanced Continual Learning with Partitioning Reservoir Sampling

Imbalanced Continual Learning with Partioning Reservoir Sampling This repository contains the official PyTorch implementation and the dataset for our

Chris Dongjoo Kim 40 Sep 18, 2022
Implementation of Basic Machine Learning Algorithms on small datasets using Scikit Learn.

Basic Machine Learning Algorithms All the basic Machine Learning Algorithms are implemented in Python using libraries Acknowledgements Machine Learnin

Piyal Banik 47 Oct 16, 2022
FedScale: Benchmarking Model and System Performance of Federated Learning

FedScale: Benchmarking Model and System Performance of Federated Learning (Paper) This repository contains scripts and instructions of building FedSca

268 Jan 01, 2023
A robust pointcloud registration pipeline based on correlation.

PHASER: A Robust and Correspondence-Free Global Pointcloud Registration Ubuntu 18.04+ROS Melodic: Overview Pointcloud registration using correspondenc

ETHZ ASL 101 Dec 01, 2022
Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

7 Jun 22, 2022
A simple consistency training framework for semi-supervised image semantic segmentation

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation PseudoSeg is a simple consistency training framework for semi-supervised image semantic s

Google Interns 143 Dec 13, 2022
📚 A collection of Jupyter notebooks for learning and experimenting with OpenVINO 👓

A collection of ready-to-run Python* notebooks for learning and experimenting with OpenVINO developer tools. The notebooks are meant to provide an introduction to OpenVINO basics and teach developers

OpenVINO Toolkit 840 Jan 03, 2023
Rank 1st in the public leaderboard of ScanRefer (2021-03-18)

InstanceRefer InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

63 Dec 07, 2022
an implementation of softmax splatting for differentiable forward warping using PyTorch

softmax-splatting This is a reference implementation of the softmax splatting operator, which has been proposed in Softmax Splatting for Video Frame I

Simon Niklaus 338 Dec 28, 2022
Code used to generate the results appearing in "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"

Train longer, generalize better - Big batch training This is a code repository used to generate the results appearing in "Train longer, generalize bet

Elad Hoffer 145 Sep 16, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022