Adaptive Object Recognition for Robotics (AORR)

This repo combines:

1. class-agnostic segmentation with wrappers for Detectron2, MMDetection, MMDeploy and TensorRT;
2. classification based on transformer feature extractor and kNN classifier.

Documentation

• Main node;
• Model deployment.

System requirements

This project was tested with:

• Ubuntu 20.04;
• ROS noetic;
• torch 1.10;
• CUDA 11.3;
• NVIDIA GTX 1050ti / RTX 3090.

Preparations:

1. clone this repo;
2. (optionally) download model checkpoint and config from GDrive and extract it in scripts/checkpoints folder.

Environment setup with Anaconda (for Detectron2 and MMDetection usage)

1. Create anaconda environment: conda env create -n conda_environment.yml;
2. conda activate segmentation_ros;
3. Install MMdet pip install openmim; mim install mmdet;
4. (Optionally) install Detectron2.

Environment setup with Docker (for any framework)

1. build docker image sudo sh build_docker.sh;
2. In line 8 in run_docker.sh change first path to your workspace folder;
3. run docker container sudo sh run_docker.sh;
4. catkin_make; source devel/setup.bash.

Usage

Main node

Run node: roslaunch computer_vision cv.launch

By default, it runs publisher. Optionally you can pass an argument mode:=service to run in service mode. Along with inference mode, this node has training mode to save new objects in classifier.

Publisher mode

Input data:

• rgb image (/camera/color/image_raw);
• aligned depth image (/camera/aligned_depth_to_color/image_raw).

Output data

As a result the node publishes a message SegmentAndClassifyResult to a topic /segm_results.

A more deeper description can be found here.

Learning of new objects

An algorithm for adding a new object:

1. place a new object in a field of view of camera so that it is the nearest detected object in a screen;
2. Call /segmentation_train_service to mask this object, get featues from feature extractor and save them;
3. Repeat previous step with different angle of view;
4. Call /segmentation_end_train_service to add all saved features to kNN.

Realsense with point cloud publishing

roslaunch computer_vision pc.launch - runs Realsense node with point_cloud=true option.