8/13/2019 9933817.pdf

1/2

Suzuki, T. et al.

Paper:

3D Terrain Reconstruction by Small Unmanned Aerial VehicleUsing SIFT-Based Monocular SLAM

Taro Suzuki, Yoshiharu Amano, Takumi Hashizume, and Shinji Suzuki

Research Institute for Science and Engineering, Waseda University

17 Kikui-cho, Shinjuku-ku, Tokyo 162-0044, Japan

E-mail: taro@power.mech.waseda.ac.jpDepartment of Aeronautics and Astronautics, School of Engineering, The University of Tokyo

7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

[Received October 6, 2010; accepted February 7, 2011]

This paper describes a Simultaneous Localization And

Mapping (SLAM) algorithm using a monocular cam-

era for a small Unmanned Aerial Vehicle (UAV). A

small UAV has attracted the attention for effective

means of the collecting aerial information. However,

there are few practical applications due to its small

payloads for the 3D measurement. We propose ex-

tended Kalman filter SLAM to increase UAV position

and attitude data and to construct 3D terrain maps us-

ing a small monocular camera. We propose 3D mea-

surement based on Scale-Invariant Feature Transform

(SIFT) triangulation features extracted from captured

images. Field-experiment results show that our pro-

posal effectively estimates position and attitude of the

UAV and construct the 3D terrain map.

Keywords: SLAM, SIFT, UAV, 3D reconstruction

1. Introduction

As aerial remote sensing using satellites, aircraft, and

manned helicopters has become increasingly widespread,

aerial laser surveys with laser scanners on aircraft and he-

licopters are being used in areas such as landslide mea-

surement in natural disasters, quality work analysis in

civil engineering, and river and levee management in

flood control. Laser surveys by manned aircraft suitablymeasure broad terrain ranges, but are so costly to operate

and flight procedures so time-consuming that they are not

conveniently applied to measuring terrain demanding fre-

quent observations. Another problem of aerial survey is

the difficulty in collecting high-resolution data due to avi-

ation regulations prohibiting low-altitude flying in such

aircraft.

In this context, remote sensing using Unmanned Aerial

Vehicles (UAVs) has attracted attention in recent re-

searches [16]. UAVs are cheaper and easier to operate

than manned craft and fly at low altitudes when acquiring

high-resolution data. Research using heavy unmanned he-

licopters has achieved precise three-dimensional (3D) ter-rain measurement [1]. Another report details 3D terrain

measurement combining cameras and laser scanners on

a large UAV [2]. Large UAVs require runways and in-

volve transport considerations that could potentially hin-

der practical application.

In contrast, small fixed-wing UAVs, which are light and

portable and require a single operator, are highly mobile

and safe enough for use in practical measurement. UAVs

weighing several kilograms can be launched manually and

collect aerial information at relatively low cost at what-

ever locations in whatever terrain. 3D terrain measure-

ment using UAVs has problems, however. Small UAVs

have such strictly limited weight that on-board equipment

such as sensors must also have limited size and weight.

This limits on-board installation of instruments such as

laser scanners, which in turn prevents directly measur-

ing 3D terrestrial coordinates. Related to the above re-

strictions preventing the use of high-precision sensors forUAV position and attitude, it is difficult to estimate ac-

curate position and attitude of small UAVs essential for

terrestrial measurement.

Small UAVs are thus more often used for image mon-

itoring than 3D terrestrial measurement. 3D topographic

mapping with small UAVs requires that vehicle accuracy

in estimating position and attitude be improved by inte-

grating data from plural sensors. Frameworks must also

enable 3D terrestrial features to be measured using small

and lightweight sensors such as monocular cameras.

Simultaneous estimation of self-positioning and map-

ping by mobile robots is called Simultaneous Localiza-tion And Mapping (SLAM). SLAM involves different ap-

proaches, particularly with wheel mobile robots [79].

Among related research using monocular cameras [3

6, 1012], monocular SLAM was designed by Davison

et al. [10] and SLAM with terrestrial cameras by Williams

et al. [11]. In such SLAM with monocular cameras, fea-

tures are automatically tracked from camera videos to es-

timate camera movement and 3D positions of features us-

ing epipolar geometry. These approaches are mainly for

3D estimation at short distances and in particular for esti-

mating positions and attitudes of cameras, making it dif-

ficult to apply them directly to 3D topographic mapping

from small UAVs in a broad range of outdoor environ-ments. Research on SLAM from UAVs [36, 12] includes

292 Journal of Robotics and Mechatronics Vol.23 No.2, 2011

8/13/2019 9933817.pdf

2/2

3D Terrain Reconstruction by Small Unmanned Aerial Vehicle

SLAM with rotary wing UAVs by Artieda et al. [3], in

which UAVs with laterally fixed cameras fly at very low

altitudes, making it difficult to produce broad-area 3D to-

pographic maps. Bryson et al. studying SLAM combin-

ing Inertial Measurement Units (IMU) and near-infrared

cameras [5, 6] used fixed-wing vehicles with near-infrared

cameras to observe markers on the ground and estimate

position and attitude called Extended Kalman Filter(EKF) SLAM. SLAM with EKF has enabled vehicle po-

sition and attitude to be estimated by observing ground

markers, although the need to place markers on the ground

itself makes it difficult to apply this to mapping unknown

environments.

We propose a SLAM algorithm for small UAVs based

on observation of natural features acquired through com-

bined Global Positioning Systems (GPS), IMU, and

monocular camera imaging. In our proposal, UAV po-

sition and attitude are estimated accurately using the

EKF algorithm on Scale-Invariant Feature Transform

(SIFT) features [13] extracted, tracked and observed from

monocular camera imaging. In addition, such SIFT fea-

tures are simultaneously estimated for 3D position to

complete 3D topographic maps.

In the sections that follow, we briefly summarize small

UAV use in our proposal and the proposed SLAM algo-

rithm. We detail the proposed SLAM algorithm and its

practice in actual flight tests, then evaluate accuracy by

comparing topographic mapping results with the Digital

Elevation Model (DEM) acquired in aerial laser surveys.

2. Small UAV and SLAM Algorithm Overview

2.1. Small UAV

The UAV we used in this research, shown in Fig. 1,

is 1.2 m long and 1.7 m wide and weighs 2 kg includ-

ing a maximum carrying weight of 500 g. Because of

weight limitations, equipment on board is limited to a

GPS receiver module, MEMS inertial sensors for 3-axial

accelerometers 3-axial gyroscopes, dynamic and static

pressure sensors, avionics containing PCs for guidance

control and computation, and a commercial digital cam-

era. Vehicle, sensor data, and airborne video status are

transmitted by radio to a Ground Control Station (GCS).

Videos taken using a commercial digital camera look-ing down from the bottom of the vehicle are transmit-

ted in VGA size (640 480, 30 fps) [14]. The UAV

makes autonomous circumvolution around predesignated

waypoints at altitudes and velocities designated prior to

flight. A typical flight plan for a small UAV is shown in

Fig. 2. The UAV is manually launched and flies along des-

ignated input routes automatically, landing via on-board

parachutes to enable it to be safely recovered even in con-

fined environs. In autonomous flights, the UAV flies at

an altitude of 80120 m at 15 m/s for 30 min, enabling

aerial information to be collected over a range of several

kilometers.

Fig. 1. UAV and configuration of onboard sensors.

Fig. 2. Typical flight plan for small UAV.

2.2. EKF-SLAM Algorithm Overview

The EKF-SLAM algorithm we propose estimates ve-

hicle locations and topographic mapping using extended

Kalman filters. Fig. 3 shows the overview of the pro-

posed EKF-SLAM algorithm. Location and velocity are

obtained by a single-frequency differential (D)-GPS re-

ceiver (4 Hz) on the UAV, together with acceleration and

angular velocity (60 Hz) measured by MEMS inertial

sensors for the SLAM algorithm. Continuous airborne

ground imaging (30 Hz) taken by the digital camera is

input to the algorithm to improve accuracy in estimat-ing UAV position and attitude and to enable topograph-

ical mapping. The 1-step SLAM cycle is set to 60 Hz

and measurement updated each time the sensor captures

data. Topographic mapping is a set of landmarks with 3D

coordinates. Environmental landmarks and features are

automatically extracted by SIFT and initial 3D landmark

coordinates determined from features tracked among plu-

ral frames added to EKF states (landmark initialization).

Since our UAV cannot carry equipment such as laser scan-

ners directly measuring distance, 3D landmark coordi-

nates are determined based on triangulation using tracked

features and previous data on vehicle position and atti-tude. Landmarks are observed by robust matching be-

tween already added states and extracted SIFT features

(landmark observation).

In EKF prediction, positions and attitude angles are cal-

culated using acceleration and angular velocity. In EKF

updating, observation is updated using position and atti-

tude observed by GPS and landmark coordinates recog-

nized from imaging and matched. Through these steps,

UAV position and attitude and 3D landmark coordinates

are thus estimated simultaneously.

The proposed SLAM algorithm cannot be appropri-

ately applied due to PC processing speed, so it is done

therefore to be conducted with the PC in the GCS using

sensor data and airborne imaging transmitted by radio.

Journal of Robotics and Mechatronics Vol.23 No.2, 2011 293