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cmake_minimum_required(VERSION 3.10)
project(RTMDet)

set_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 11)

# Find OpenCV
find_package(OpenCV REQUIRED)

# Set the CUDA paths. Update this path to your CUDA installation.
set(CUDA_TOOLKIT_ROOT_DIR /usr/local/cuda-11.8)

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With this Python scripts, you can infer your images with RTMDet TensorRT models.

From this link you can get Python models and convert them to ONNX and TensorRT models

You need following files to run this script:

1. TensorRT Model: You can create from the link
2. mmdetection tensorrt plugin: it created with mmdeploy and you should load this plugin in script (line 61) (it locates under mmdeploy/mmdeploy/lib after you compile mmdeploy).
3. image: any image

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Introduction

This documentation shows you how can you create color maps with LIO-SAM. LIO-SAM is a real-time lidar-inertial odometry package. With our modifications you can create color maps using your multiple number of cameras. You can reach our fork with this link

Required Data

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Introduction

This is a tutorial to create SLAM map with LIO-SAM without dynamic objects using these:

• https://github.com/TixiaoShan/LIO-SAM/tree/ros2
• https://github.com/autowarefoundation/autoware.universe/tree/main/perception/lidar_centerpoint
• https://github.com/davutcanakbas/dynamic_object_removal

Required Data

To run LIO-SAM without dynamic objects, there is need for ROS2 bag file and ROS2 bag file should contain these topics:

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#! /usr/bin/env bash

# Colors
RED='\033[0;31m'
GREEN='\033[0;32m'
BLUE='\033[0;34m'
PURPLE='\033[0;35m'
NC='\033[0m' # No Color

# Emojis