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Foresee Navigation

Semantic-Segmentation based autonomous indoor navigation for mobile robots.

The Problem

The principle sensor for autonomously navigating robots is the LiDAR. However, low-cost 2D LiDARs cannot detect many man-made obstacles such as mesh-like railings, glass walls, or descending staircases, and 3D LiDARs that can detect some of these are prohibitively expensive.

This project is a proof-of-concept showing that Deep Learning can provide a solution for detecting such obstacles.

Video Demo

Video Demo

Our Approach

The basis of our strategy is to integrate detected obstacle locations into proven tools for autonomous navigation from the Robot Operating System.

The process begins by segmenting the floor using DeepLab with ResNet V2 50. To learn about how this works in more detail, see this.

Model Inference

A perspective transform is then applied to get top-down view of safe and unsafe areas. The resulting image is cut into discrete sectors, and the lowest point in each sector is calculated. These points, after a linear transform to real-world robot-relative coordinates, form a ROS pointcloud that is fed into our navigation stack.

Point Cloud, Top Down

This data is then fed into SLAM through gmapping and we use pathfinder and nonlinear reference tracking to follow a path autonomously. See more information about pathfinder_ros and reftracking_ros.

Our Platform

This repository includes an implementation of the above based on the Jackal robot platform with a NVIDIA Jetson TX2 inside. We integrate the above pipeline with a low-cost RPLiDAR A1. For a camera we used an iBUFFALO BSW20KM11BK mounted about 25cm above the ground.

Robot Image

Repository Structure

The ROS workspace that is deployed to the robot is located in ros_workspace. Inside src, robot_control contains the coordinating launch files and safety nodes and deep_segmentation contains the Deep Learning pipeline.

The superpixel-seg directory contains an earlier approach to segmenting the floor based on superpixels and a random forest classifier. SXLT contains a small labeling tool for segmentation.

Since this project depends on OpenCV, (and superpixel-seg on OpenCV_contrib), a version of JetsonHacks's buildOpenCVTX2 that allows enabling and disabling certain contrib modules is included.

Usage

Our code assumes a Jackal development platform with a BNO055 IMU integrated, an RPLidar A1 with udev rules configured, and a 120-degree-fov iBUFFALO BSW20KM11BK with distortion and mounting characteristics identical to our own.

Software dependencies include OpenCV, ROS, NumPy, and Tensorflow. To get segmentation to work, it is highly recommended that you use CUDA 8 and Tensorflow 1.3. CUDA 8 can be installed via JetPack 3.1 without re-flashing.

Begin by running provision.sh in the root directory. This downloads files that could not be re-distributed for licensing reasons and the binary model files.

To run the code, first setup the ROS Workspace. cd ros_workspace, then execute setup.sh, and finally source install_isolated.sh. This will install all necessary ROS packages and compile certain ones from source inline. If you want to edit the code, remember to re-source install_isolated.sh after each edit. Finally, roslaunch robot_control auton.launch and, in a separate terminal, rosrun driver_station ds.py. To enable, use C-x C-e and press space to disable.

License

Apache 2.0, see LICENSE.

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