zju3dv / Pvio
Licence: apache-2.0
Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors
Stars: ✭ 198
Labels
Projects that are alternatives of or similar to Pvio
Arcoreinsideouttrackinggearvr
Inside Out Positional Tracking (6DoF) for GearVR/Cardboard/Daydream using ARCore v1.6.0
Stars: ✭ 150 (-24.24%)
Mutual labels: slam
Pop up slam
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Stars: ✭ 164 (-17.17%)
Mutual labels: slam
Pyicp Slam
Full-python LiDAR SLAM using ICP and Scan Context
Stars: ✭ 155 (-21.72%)
Mutual labels: slam
Lili Om
LiLi-OM is a tightly-coupled, keyframe-based LiDAR-inertial odometry and mapping system for both solid-state-LiDAR and conventional LiDARs.
Stars: ✭ 159 (-19.7%)
Mutual labels: slam
Visual Gps Slam
This is a repo for my master thesis research about the Fusion of Visual SLAM and GPS. It contains the research paper, code and other interesting data.
Stars: ✭ 175 (-11.62%)
Mutual labels: slam
Ssl slam
SSL_SLAM: Lightweight 3-D Localization and Mapping for Solid-State LiDAR IEEE RA-L 2021
Stars: ✭ 144 (-27.27%)
Mutual labels: slam
Deepmatchvo
Implementation of ICRA 2019 paper: Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Stars: ✭ 178 (-10.1%)
Mutual labels: slam
Pythonrobotics
Python sample codes for robotics algorithms.
Stars: ✭ 13,934 (+6937.37%)
Mutual labels: slam
Pangolin
Python binding of 3D visualization library Pangolin
Stars: ✭ 157 (-20.71%)
Mutual labels: slam
Recent slam research
Track Advancement of SLAM 跟踪SLAM前沿动态【2021 version】
Stars: ✭ 2,387 (+1105.56%)
Mutual labels: slam
Surfelwarp
SurfelWarp: Efficient Non-Volumetric Dynamic Reconstruction
Stars: ✭ 149 (-24.75%)
Mutual labels: slam
Hypharos minicar
1/20 MiniCar: An ackermann based rover for MPC and Pure-Pursuit controller
Stars: ✭ 194 (-2.02%)
Mutual labels: slam
Anms Codes
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Stars: ✭ 174 (-12.12%)
Mutual labels: slam
Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors
Jinyu Li, Bangbang Yang, Kai Huang, Guofeng Zhang, and Hujun Bao*
PRCV 2019, LNCS 11859, pp. 283–295, 2019.
How to use
For compilation:
- Install the dependencies: Eigen, Ceres Solver and OpenCV.
- Clone the repository.
- Build with
mkdir -p build && cd build && cmake -DCMAKE_BUILD_TYPE=Release .. && make -j8
, you will need a compiler supporting C++17. - Tested in Ubuntu 18.04 (with GCC 9.0 and CMake 3.11), and macOS 10.14.
For execution:
-
./pvio-pc [data_scheme]://[data_path] [config_yaml_path]
- e.g.
- For EuRoC Dataset:
build/pvio-pc/pvio-pc euroc:///Data/EuRoC/V1_01_easy/mav0 config/euroc.yaml
- For TUM-VI Dataset:
build/pvio-pc/pvio-pc tum:///Data/TUM_VI/dataset-room1_512_16/mav0 config/tum_vi.yaml
- For EuRoC Dataset:
- e.g.
- The trajectory will be written in
trajectory.tum
.
Publication
If you use this source code for your academic publication, please cite the following paper.
@inproceedings{PRCV-LiYHZB2019,
author={Jinyu Li and Bangbang Yang and Kai Huang and Guofeng Zhang and Hujun Bao},
title = {Robust and Efficient Visual-Inertial Odometry with Multi-plane Priors},
booktitle = {Pattern Recognition and Computer Vision - Second Chinese Conference,
{PRCV} 2019, Xi'an, China, November 8-11, 2019, Proceedings, Part {III}},
series = {Lecture Notes in Computer Science},
volume = {11859},
pages = {283--295},
publisher = {Springer},
year = {2019}
}
Acknowledgements
This work is affliated with ZJU-SenseTime Joint Lab of 3D Vision, and its intellectual property belongs to SenseTime Group Ltd.
Copyright
Copyright (c) ZJU-SenseTime Joint Lab of 3D Vision. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].