Research on Event Accumulator Settings for Event-Based SLAM

Overview

Research on Event Accumulator Settings for Event-Based SLAM

This is the source code for paper "Research on Event Accumulator Settings for Event-Based SLAM". For more details please refer to https://arxiv.org/abs/2112.00427

1. Prerequisites

See dv_ros and VINS-Fusion

2. Build

cd ~/catkin_ws/src
git clone https://github.com/robin-shaun/event-slam-accumulator-settings.git
cd ../
catkin_make 
source ~/catkin_ws/devel/setup.bash

3. Demo

We evaluate the proposed method quantitatively on the Event Camera Dataset. This demo takes the dynamic_6dof sequence as example.

First, start dv_ros. Notice that the event accumulator depends on the timestamp, so when you restart the dataset or davis driver, you should restart dv_ros.

roslaunch dv_ros davis240.launch

And then, start VINS-Fusion

roslaunch vins vins_rviz.launch
rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/davis/rpg_240_mono_imu_config.yaml

Finally, play the rosbag

cd ~/catkin_ws/src/event-slam-accumulator-settings/dataset
rosbag play dynamic_6dof.bag

4. Run with your devices

We have tested the code with DAVIS240 and DAVIS346. If you want to run with your devices, the most important thing to do is calibrate the event camera and imu. We advise to use Kalibr with traditional image from APS and IMU, because the intrinsics and extrinsics are almost the same for APS and DVS.

If you want to compare the event-based VINS Fusion with traditional VINS Fusion with DAVIS346, you should use this code. Because the frame from APS of DAVIS346 sometimes changes the size, we do some modification for VINS-Fusion.

5. Acknowledgements

Thanks for dv_ros and VINS-Fusion.

Owner
Robin Shaun
Aerospace Engineering
Robin Shaun
Code to reproduce experiments in the paper "Explainability Requires Interactivity".

Explainability Requires Interactivity This repository contains the code to train all custom models used in the paper Explainability Requires Interacti

Digital Health & Machine Learning 5 Apr 07, 2022
Neural Turing Machines (NTM) - PyTorch Implementation

PyTorch Neural Turing Machine (NTM) PyTorch implementation of Neural Turing Machines (NTM). An NTM is a memory augumented neural network (attached to

Guy Zana 519 Dec 21, 2022
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records

HiPAL Code for KDD'22 Applied Data Science Track submission -- HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electro

Hanyang Liu 4 Aug 08, 2022
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

PyGrid is a peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft. PyGrid is also the central serv

OpenMined 615 Jan 03, 2023
🌈 PyTorch Implementation for EMNLP'21 Findings "Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer"

SGLKT-VisDial Pytorch Implementation for the paper: Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer Gi-Cheon Kang, Junseok P

Gi-Cheon Kang 9 Jul 05, 2022
DISTIL: Deep dIverSified inTeractIve Learning.

DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.

decile-team 110 Dec 06, 2022
TLDR: Twin Learning for Dimensionality Reduction

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self

NAVER 105 Dec 28, 2022
A PyTorch implementation of PointRend: Image Segmentation as Rendering

PointRend A PyTorch implementation of PointRend: Image Segmentation as Rendering [arxiv] [Official Implementation: Detectron2] This repo for Only Sema

AhnDW 336 Dec 26, 2022
Encoding Causal Macrovariables

Encoding Causal Macrovariables Data Natural climate data ('El Nino') Self-generated data ('Simulated') Experiments Detecting macrovariables through th

Benedikt Höltgen 3 Jul 31, 2022
Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ONNX-CoEx-Stereo-Depth-estimation Python scripts form performing stereo depth estimation using the CoEx model in ONNX. Stereo depth estimation on the

Ibai Gorordo 8 Dec 29, 2022
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches

BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applic

SAFARI Research Group at ETH Zurich and Carnegie Mellon University 19 Dec 26, 2022
NICE-GAN — Official PyTorch Implementation Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

NICE-GAN-pytorch - Official PyTorch implementation of NICE-GAN: Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

Runfa Chen 208 Nov 25, 2022
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

13 Dec 01, 2022
Generalized Data Weighting via Class-level Gradient Manipulation

Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas

18 Nov 12, 2022
For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

ImgAlign For auto aligning, cropping, and scaling HR and LR images for training image based neural networks Usage Make sure OpenCV is installed, 'pip

15 Dec 04, 2022
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized

VQGAN-CLIP-Docker About Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized This is a stripped and minimal dependency repository for running loca

Kevin Costa 73 Sep 11, 2022
Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
torchsummaryDynamic: support real FLOPs calculation of dynamic network or user-custom PyTorch ops

torchsummaryDynamic Improved tool of torchsummaryX. torchsummaryDynamic support real FLOPs calculation of dynamic network or user-custom PyTorch ops.

Bohong Chen 1 Jan 07, 2022
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
Pre-trained Deep Learning models and demos (high quality and extremely fast)

OpenVINO™ Toolkit - Open Model Zoo repository This repository includes optimized deep learning models and a set of demos to expedite development of hi

OpenVINO Toolkit 3.4k Dec 31, 2022