automated elevation mapping of unstructured outdoor areaspreprocessing of stereo and velodyne data...

rrlab.cs.uni-kl.de

Hannan Ejaz KeenRobotics Research Lab

Department of Computer ScienceUniversity of Kaiserslautern, Germany

Automated Elevation Mapping of Unstructured Outdoor Areas

rrlab.cs.uni-kl.de

Motivation

The height of fields - an interesting parameter

Approximation of field height is rather easy on flat fieldsApproximation of field height becomes a problem if the

field has variable elevation

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Goal

Extract precise elevation map of field

rrlab.cs.uni-kl.de

We will discuss…

GPS-IMU Approach

Need of More Data!

Sensors and Data gathering

Implemented Methodology

Preprocessing

Plane Extraction

Scan Transformation

Pointloud Merge and Octomap

Surface Generation

Geotiff Representation

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GPS-IMU Approach

Proposed Sensors IMU Stereo Camera GPS

Localization Fused all three sensors Precise localization Used as such

Mapping Update points at each run

Map based on waypoints only

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Need of More Data!

Less precise map due to limited points Difficult to update points in previously stored geotiff map

Addition of velodyne and stereo data No update required

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Sensors and Data gathering

Data from two different sources i.e. from ground vehicle and octocopter

Multisensor data fusion to construct precise and computationally sound elevation maps

Sensors under consideration are Imu, rtk-gps, stereo camera and velodyne (ground vehicle) Imu, gps, stereo camera (octocopter)

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Implemented Methodology - Steps

Preprocessing of stereo and velodyne data Extract plane by Ransac plane model Transform all sensor at a specific frame of reference (GPS

as reference in our case) Point cloud merge using octomaps Fill out the holes in map by cubic spline interpolation GeoTIFF Representation

Preprocessing RANSAC TransformationPoint Cloud

MergeSpline

InterpolationGeoTIFF

Representation

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Preprocessing

Huge variation in point densities in point clouds Measurement errors lead to sparse outliers Nearest neighbor assuming the distribution as gaussian with

mean and standard deviation Dynamic tuneable parameters

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Iterative process to estimate the parameters of mathematical model

Assumption – data comprises of both inliers and outliers Outliers do not fit the model in any circumstances

Plane Extraction - RANSAC

Outline:

1. Choose a small subset of points uniformly at random

2. Fit a model to that subset

3. Find all remaining points that are “close” to the model and reject the rest as

outliers

4. Do this many times and choose the best model

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RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

RANSAC

Source: R. Raguram

rrlab.cs.uni-kl.de

Trees, poles or animals on field are considered as outlier Extraction of field assuming plane model and removing

outliers

Plane Extraction - Results

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Transformation of scan with respect to first scan Orientation of sensors are aligned with GPS 8x fast as compared to Iterative Closest Point - ICP

Scan Transformation

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Scan Transformation

Transformation of scan with respect to first scan Orientation of sensors are aligned with GPS 8x fast as compared to Iterative Closest Point - ICP

rrlab.cs.uni-kl.de

Scan Transformation - Results

Transformation of scan with respect to first scan Orientation of sensors are aligned with GPS 8x fast as compared to Iterative Closest Point - ICP

rrlab.cs.uni-kl.de

Point Cloud merge and Octomap

Scan merge iteratively

Consumes 33Mb memory in one second by a velodyne scan as each scan gives upto 1.39 million points per second

High frequency, approximately 60% redundant points

Sensor uncertainty gives some outliers which are not in every scan

A nearest neighbor caters the problem of redundancy Use of octomap for fast searching and insertion

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Octomap

*A. Hornung,. K.M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, "OctoMap: An Efficient Probabilistic 3D Mapping Framework Based

on Octrees" in Autonomous Robots, 2013; DOI: 10.1007/s10514-012-9321-0.

Implements 3d occupancy grid mapping approach C++ Library* Based on Octree Octrees are the extension on Quadtree to three dimensions

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Octomap

*A. Hornung,. K.M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, "OctoMap: An Efficient Probabilistic 3D Mapping Framework Based

on Octrees" in Autonomous Robots, 2013; DOI: 10.1007/s10514-012-9321-0.

Implements 3d occupancy grid mapping approach C++ Library* Based on Octree Octrees are the extension on Quadtree to three dimensions

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Point Cloud merge and Octomap (Cont.)

Velodyne-Stereo merge cloud in oct0map representation

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Point Cloud merge and Octomap (Cont.)

GPS Waypoints (green) and Velodyne-Stereo merge cloud (purple)

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Surface Generation

Interpolate the cloud – Cubic BSpline Interpolation Linear combination of control points and BSpline basis

function Local control over the curve Continuous at merging points Continuous first and second derivative where they join First and Second derivatives are equal for merging points

Image Source: http://m2matlabdb.ma.tum.de/download.jsp?MC_ID=7&SC_ID=7&MP_ID=485

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Surface Generation

Pcl library – surface_fitting DGM data having less than 1m accuracy as ground truth 87% of precision

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GeoTIFF Representation

Georeferenced Tagged Image File Format An interchange format for georeferenced raster imagery