The Evaluation of Field of View Width in Stereovision-based Visual Homing: Data repository

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The six databases are briefly overviewed in Table 1 below. The picture to the left on each row is a single representative picture from the database.

Data Collection Procedure

A spatial grid is superimposed on the area to be recorded, and panoramic visual imagery is collected at each grid cell. The information is used as follows. A robot begins a simulation run with a home position grid cell and a start position grid cell. The visual information the robot receives is the stored imagery for the grid cell it occupies. A motion vector is calculated by comparing this imagery to the home grid cell imagery. This motion may move the robot to another grid cell, and the imagery comparison and motion calculation continues. Homing algorithms different in how these comparisons are done, how the motion vector is calculated, and how termination is decided.

The robot used in our visual homing research is a Pioneer 3-AT robot with a Bumblebee2 stereo-camera mounted on a Pan-Tilt (PT) base. The Bumblebee2 with 3.8 mm lens has a 66 deg horizontal FOV for each camera. This limited FOV is one key challenge in building a visual homing database since each grid cell in the database must contain 360 deg of imagery; A robot can arrive at a grid location with any orientation and the simulation should be able to retrieve the imagery for that orientation. This issue is addressed by using the PT base to collect 10 visual and stereo depth images at orientations 36 deg apart, starting at 0 deg with respect to the X axis. The visual image stored is the left image from the stereo pair. The RH coordinate frame has the X axis along the direction in which the robot is facing, the Y pointing left, and is centered on the robot. The final angle requires the robot to be reoriented (in addition to the PT unit motion) due to PT unit pan limits.

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A note on folder naming and orientation labeling orientation labeling

Histogram smoothing was performed on the visual images of all outdoor grids, in order to compensate for lighting conditions.

A statistical filtering program, from the point cloud library, was executed on all stereo-depth files to clean up some stereo noise. The parameters of the point cloud statistical filter were a meanK of 50 and a standard deviation threshold of 0.2.

The visual data

* grid11Image.tar.gz: Grid 11 4x4 grid image data.
* grid12Image.tar.gz: Grid 12 4x4 grid, image data.
* grid13Image.tar.gz: Grid 13 4x4 grid, image data.
* grid14Image.tar.gz: Grid 14 4x4 grid, image data.
* grid15Image.tar.gz: Grid 14 7x7 grid, image data.
* grid16Image.tar.gz: Grid 16 7x7 grid, image data.

The depth data

* grid11Depth.tar.gz: Grid 11 4x4 grid depth data.
* grid12Depth.tar.gz: Grid 12 4x4 grid, depth data.
* grid13Depth.tar.gz: Grid 13 4x4 grid, depth data.
* grid14Depth.tar.gz: Grid 14 4x4 grid, depth data.
* grid15Depth.tar.gz: Grid 14 7x7 grid, depth data.
* grid16Depth.tar.gz: Grid 16 7x7 grid, depth data.

The effect of Horizontal Field of View on Stereovision-based Visual Homing: Data repository

D.M. Lyons, B. Barriage, L. Del Signore,

Abstract of the paper

Overview of the Data Repository

The six databases are briefly described in Table 1 below.

Visual Homing with Sterevision Databases (HSVD)

The HSVD project is an extension of the work previously done with HSV, a method of navigation devleoped for a robotic platform based upon common navigation techniques of insects. This extension results in an HSV program that is compatible with a user's database in order for simulated navigation of a grid between a starting location and a goal location, using their respective images.This is perfomed by comparing key features found belonging to both a concatenated set of home images and a concatenated set of current location images. Through this we hope to create a working simulation of accurate and efficient traversal of a database, consisting of location coordinates and orientations, using some of the key concepts implemented earlier in the HSV project.

Integrating Stereovision Database into the HSV program

Execution begins as the program recieves coordinates for both the current and goal locations, along with the orientation of the robot in each of these instances. Just prior to this, the user will be tasked with entering the grid number of the database which they would like to use. When entering the coordinates, make sure the second value is negative; this is due to the nature of the robot's axis.

COORDINATE FRAME CONVENTIONS: A zero pose of the robot has it at the origin facing +X direction. Thus a +ve angle is CCW and turns towards +ve Y and a -ve angle is CW and turns towards -ve Y.

The orientation and location will then be used to determine the proper images to use with the database grid, along with a selection of these images to represent the forward facing one hundred eighty degrees of the robot. These images are then concatenated into two panoramic images, one representing the goal location and one representing the current location, which are examined using sift features to find keypoints which can be compared between the images of the two locations. Once these keypoints are compared and a subset are determined to be valid, motion can be determined by comparing the theta and distance values for each of the matches points. Both of these calculations are performed within vh.cpp and the averages are used to determine the motion of the robot.

It is important to note that the distance should be calculated using the Euclidian formula for distance and that a negative value should be reachable. The robot will move a portion of the way so that compensation can be performed in the instance of an incorrect turn or move, which will occasionally occur during execution. Attention should be paid to the scale and gain values within main.cpp which will have a significant affect on the program; generally, the scale should be 1000 and the gain values should be between 0.3-0.6, but no higher than 1.0. In addition, ample time and steps must be provided because the robot may still reach its location despite taking a great deal of time to do so.

Repository Information

1. Files below labelled GRIDXX_grids contain visual image folders, while those labelled GRIDXX_cleanDump contain stereo depth data.

2. Histogram smoothing was performed on the visual images of GRID14, an outdoor grid, in order to compensate for lighting conditions.

3. A statistical filtering program, from the point cloud library, was executed on stereo-depth to clean up some stereo noise data. The parameters of the point cloud statistical filter were a meanK of 50 and a standard deviation threshold of 0.2.

Project Figures & Diagrams

• Depth Figures for HSVD Conference Paper: GRID 14
  

• Image Figures for HSVD Conference Paper: GRID 14
  

* Side by Side Comparison of HSVD Frame & Depth Images: GRID 14

Datasets

* GRID13_grids.tar.gz: Grid folder for HSVD project. 10/27/16
* GRID13_cleanDump.tar.gz: Clean Data Dump folder for HSVD project. 11/7/16
* GRID12_cleanDump.tar.gz: Clean Data Dump folder for HSVD project. 11/7/16
* GRID11_cleanDump.tar.gz: Clean Data Dump folder for HSVD project. 11/7/16
* GRID14_cleanDump.tar.gz: Clean Data Dump folder for HSVD project. 11/7/16
* GRID14_grids.tar.gz: Grid folder for HSVD project post smoothing. 12/5/16
* GRID12_grids.tar.gz: Grid folder for HSVD project post smoothing. 12/5/16
* GRID11_grids.tar.gz: Grid folder for HSVD project. 2/23/17

This data is provided for general use without any warranty or support. Please send any email questions to dlyons@fordham.edu, bbarriage@fordham.edu, and ldelsignore@fordham.edu.

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