OTTFFIVE

This project is the result of my undergraduate dissertation. The localization in dynamic environment is to deploy TensorRT optimized YOLOX in the front end of ORB-SLAM3 for object detection, and then eliminate all points belonging to the human bounding box. At the same time, the semantic information is sent to the mapping module to dye the 3D point cloud. The disadvantage of this project is that in the localization module, only the points belonging to people are processed, because people are dynamic most of the time. In the mapping module, we did not segment semantic objects accurately, resulting in wrong coloring of point clouds of other objects.

Language:C++GPL-3.0000

DF-VO

Depth and Flow for Visual Odometry

MIT000

FAST_LIO

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

Language:C++GPL-2.0010

FAST_LIO_SAM

Front_end : fastlio2 Back_end : lio_sam

Language:Makefile000

hector_slam

hector_slam contains ROS packages related to performing SLAM in unstructed environments like those encountered in the Urban Search and Rescue (USAR) scenarios of the RoboCup Rescue competition.

000

leetcode-master

《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀

Language:Shell000

LiDAR-MOS

(LMNet) Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data (RAL/IROS 2021)

Language:PythonMIT000

Lidar_IMU_Localization

Lidar-IMU Localization System with Prior Map Constraint and Lio Constraint

BSD-3-Clause000

LIO-PPF

[IROS 2023] Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking

MIT000

LIO_SAM_6AXIS

LIO_SAM for 6-axis IMU and low-cost GNSS.

Language:C++000

liorf_localization

A simple location system based on a priori map, which is based on the lio-sam framework

Language:C++000

NeRF-SLAM

NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields. https://arxiv.org/abs/2210.13641 + Sigma-Fusion: Probabilistic Volumetric Fusion for Dense Monocular SLAM https://arxiv.org/abs/2210.01276

Language:PythonBSD-2-Clause000