🛠️ SLAMcore SLAM Utilities

Overview

slamcore_utils

PyPI version Code style: black

Description

This repo contains the slamcore-setup-dataset script. It can be used for installing a sample dataset for offline testing and evaluation of SLAMcore's Localization and Mapping capabilities.

Currently the following types of datasets are supported:

Usage

After installation the script should be available in your path. Executing it will guide you through a list of questions in order to properly setup a sample SLAM dataset.

Here is a sample execution of the said script to enable processing of the TUM-VI dataset-room4_1024_16

setup-dataset2

Here's the same execution for the OpenLORIS cafe1-1 dataset

setup-dataset1

And here's the execution guiding the user to the right download page, when the datasets are not available locally yet.

setup-dataset3

Installation

Install it directly from PyPI:

pip3 install --user --upgrade slamcore_utils[tqdm]

# Or if you don't want tqdm's polished progress bars
pip3 install --user --upgrade slamcore_utils
I don't want to have to install it

Make sure the project dependencies are installed:

pip3 install -r requirements.txt

Then adjust your PYTHONPATH variable and run accordingly:

git clone https://github.com/slamcore/slamcore_utils
cd slamcore_utils
export PYTHONPATH=$PYTHONPATH:$PWD
./slamcore_utils/scripts/setup_dataset.py
I don't want to install any of your dependencies in my user's install directory

Consider using either pipx or poetry to install this package and its dependencies isolated in a virtual environment:

git clone https://github.com/slamcore/slamcore_utils
poetry install
poetry shell

# the executables should now be available in your $PATH
setup-dataset

About SLAMcore

SLAMcore offers commercial-grade visual-inertial simultaneous localisation and mapping (SLAM) software for real-time autonomous navigation on robots and drones. Request access today to get started.

Owner
SLAMcore
SLAMcore
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Readme File for "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" by Ham, Imai, and Janson. (2022) All scripts were written and

0 Jan 27, 2022
PyTorch implementation of "LayoutTransformer: Layout Generation and Completion with Self-attention"

PyTorch implementation of "LayoutTransformer: Layout Generation and Completion with Self-attention" to appear in ICCV 2021

Kamal Gupta 75 Dec 23, 2022
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching(CVPR2021)

CFNet(CVPR 2021) This is the implementation of the paper CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching, CVPR 2021, Zhelun Shen, Yuch

106 Dec 28, 2022
MonoRCNN is a monocular 3D object detection method for automonous driving

MonoRCNN MonoRCNN is a monocular 3D object detection method for automonous driving, published at ICCV 2021. This project is an implementation of MonoR

87 Dec 27, 2022
Combining Diverse Feature Priors

Combining Diverse Feature Priors This repository contains code for reproducing the results of our paper. Paper: https://arxiv.org/abs/2110.08220 Blog

Madry Lab 5 Nov 12, 2022
"Domain Adaptive Semantic Segmentation without Source Data" (ACM MM 2021)

LDBE Pytorch implementation for two papers (the paper will be released soon): "Domain Adaptive Semantic Segmentation without Source Data", ACM MM2021.

benfour 16 Sep 28, 2022
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.

Jaeyeon Ahn 2 Mar 22, 2022
Driller: augmenting AFL with symbolic execution!

Driller Driller is an implementation of the driller paper. This implementation was built on top of AFL with angr being used as a symbolic tracer. Dril

Shellphish 791 Jan 06, 2023
基于深度强化学习的原神自动钓鱼AI

原神自动钓鱼AI由YOLOX, DQN两部分模型组成。使用迁移学习,半监督学习进行训练。 模型也包含一些使用opencv等传统数字图像处理方法实现的不可学习部分。

4.2k Jan 01, 2023
Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

16 Nov 28, 2022
Official PyTorch implementation of the paper Image-Based CLIP-Guided Essence Transfer.

TargetCLIP- official pytorch implementation of the paper Image-Based CLIP-Guided Essence Transfer This repository finds a global direction in StyleGAN

Hila Chefer 221 Dec 13, 2022
Source code for our paper "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash"

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash Abstract: Apple recently revealed its deep perceptual hashing system NeuralHash to

<a href=[email protected]"> 11 Dec 03, 2022
LAnguage Model Analysis

LAMA: LAnguage Model Analysis LAMA is a probe for analyzing the factual and commonsense knowledge contained in pretrained language models. The dataset

Meta Research 960 Jan 08, 2023
GANSketchingJittor - Implementation of Sketch Your Own GAN in Jittor

GANSketching in Jittor Implementation of (Sketch Your Own GAN) in Jittor(计图). Or

Bernard Tan 10 Jul 02, 2022
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

This is a release of our VIMPAC paper to illustrate the implementations. The pretrained checkpoints and scripts will be soon open-sourced in HuggingFace transformers.

Hao Tan 74 Dec 03, 2022
Official repo for our 3DV 2021 paper "Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements".

Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements Yu Rong, Jingbo Wang, Ziwei Liu, Chen Change Loy Paper. Pr

Yu Rong 41 Dec 13, 2022
Pytorch implementation of "Training a 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet"

Token Labeling: Training an 85.4% Top-1 Accuracy Vision Transformer with 56M Parameters on ImageNet (arxiv) This is a Pytorch implementation of our te

蒋子航 383 Dec 27, 2022
DeepGNN is a framework for training machine learning models on large scale graph data.

DeepGNN Overview DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features in

Microsoft 45 Jan 01, 2023
Code and datasets for TPAMI 2021

SkeletonNet This repository constains the codes and ShapeNetV1-Surface-Skeleton,ShapNetV1-SkeletalVolume and 2d image datasets ShapeNetRendering. Plea

34 Aug 15, 2022
A library for optimization on Riemannian manifolds

TensorFlow RiemOpt A library for manifold-constrained optimization in TensorFlow. Installation To install the latest development version from GitHub:

Oleg Smirnov 83 Dec 27, 2022