Running Pepper's starred repositories
deepfuturegaze_gan
Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks
fusion_pointclouds
fusion_pointclouds 主要目的为Ubuntu环境下无人车多激光雷达标定之后, 将多个激光雷达点云话题/坐标系 通过PCL (Point Cloud Library)融合为 一个ros点云话题,以便于后期点云地面分割与地面处理等等。
spinningup
An educational resource to help anyone learn deep reinforcement learning.
image-matching-webui
🤗 image matching toolbox webui
orbslam2_learn
Learn ORBSLAM2 and divide the source code into many parts according to their function which can be easily built by the learner from my blog.
deep-visual-geo-localization-benchmark
Official code for CVPR 2022 (Oral) paper "Deep Visual Geo-localization Benchmark"
cnnimageretrieval-pytorch
CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch
yolov4-tiny-pytorch
这是一个YoloV4-tiny-pytorch的源码,可以用于训练自己的模型。
Map-based-Visual-Localization
A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.
Hierarchical-Localization
Visual localization made easy with hloc
visloc-relapose
A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]
CVPR2024-Paper-Code-Interpretation
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集,极市团队整理
Self_Driving_Car
This Project gives a glimpse of a real-world self-driving car.This is a model totally based on Nvidia Self Driving Car model.A Convolutional neural network(CNN) is trained to map raw pixels from a single front-facing camera directly to steering commands.
cyber_meld
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
Self-Driving-Car-Path-Planning-Project
Navigate to goal with High Definition(HD) Map, Sensor Fusion, Finite State Machine and Optimise by Cost Function.
Object-Recognition-for-Autonomous-Driving-System-MATLAB-project
The goal of this project is to provide object detection and information on environment model on traffic activity which helps autonomous vehicles or surveillance systems. Computer vision is an essential component for autonomous scars. Accurate detection of vehicles, street buildings, pedestrians, and road signs could assist self-driving cars the drive as safely as humans. However, object detection has been a challenging task for years since images of objects in the real-world environment are affected by illumination, rotation, scale, and occlusion. A unified object detection model, You Only Look Once (YOLO), is used which could directly regress from the input image to object class scores and positions. In this project, we applied YOLO to two different datasets to test its general applicability. We fully analyzed its performance from various aspects on KITTI data set which is specialized for autonomous driving. We proposed a novel technique called memory map, which considers inter-frame information, to strengthen YOLO's detection ability in the driving scene. We broadened the model's applicability scope by applying it to a new orientation estimation task. For this project objective is to provide a information of quality and environmental model on traffic activity and to signal potentially anomalous situation and also apply various machine learning models for object detection such as SVM and CNN and compare and contrast the results with YOLO .
SEMANTIC_VISUAL_SUPPORTED_ODEMETRY
semantic visual slam for monocular and stereo camera devices
hdl_graph_slam
3D LIDAR-based Graph SLAM
awesome-visual-slam
:books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers