Kalman filter based range estimation for autonomous navigation using ...

N90-22238 Kalman

Filter

Based

Range

Estimation

for Autonomous

Banavar NASA Rotorcraft surrounding enemy.

operating terrain,

The piloting

on-board

environments

tool, which

The planning (Cheng

to devote

more

has the potential

of rotorcraft

low-altitude

and Sridhar,

1988).

resolution

information system

to modify

forward

looking

detection

to indicate

to assess

are detection There

using

the nominal infrared

are many

in the rotorcraft

involves

parameters

buildings,

sensor

methods.

computed

using

differential relative

an onboard

equations,

coordinates

translational

inertial

navigation

filter (Sridhar

and the Earth

coordinates

used in a predictive

mode

features

effort

extraction

in the feature

for different rotorcraft not, however, include results

obtained

restricted

to linear

wires

Initially,

motion

be evaluated generation

presented

The method further

the

problems

and near-field

and a nominal

and transmission

passive

the current using

rotational

system.

Given

and Phatak, of objects

imaging

sensors

for

for obstacle

avoidance

position.

a sequence

of images.

The approach

ways: (i) we do not attempt lies in recursive algorithms.

velocity

and attitude)

a sequence

of images,

using

The Kalman

to a significant

The performance

to The

are assumed

1988) can be used to estimate leading

This

such as

will be considered

rotorcraft

on the ground.

in the images,

towers.

into the navigation/guidance

(LLLTV)

of range

step of the algorithm.

the Kalman in order

of range

a simple produces using

the image

to real images during

recursive good range

several

part of the algorithm

filter for use with actual

to reduce

of this algorithm

to the estimation

We have images.

warn

image-object both the

filter

reduction

of three different

to be

can also be of search

Kalman

filters

maneuvers were examined in Sridhar and Phatak, 1988. This previous study did the processing of real images. The purpose of this paper is to summarize early

in extending

from the application proceeding

to track

planning

of goals

The two basic requirements

from

velocity,

a Kalman

path,

challenging

into far-field

and integrated

of the rotorcraft.

to the estimation

(position,

The development

flight

presents

the selection

used in this analysis differs from previous methods in two significant estimate the rotorcraft's motion from the images, and (ii) our interest rotorcraft

by the

is based on a priori information and requires a detailed for even the best surveyed landscape will not have

of the objects

approaches

to utilize detected

activities.

obstacles,

can be divided

such as trees,

of passive

estimation

Sensors

task and the pilot will need help from

and low-light-level-television

the limitation

and range

Imaging

surface

the risk of being

obstacles

detected

planning

an onboard

trajectory

(FLIR)

to the Earth's

to minimize

time to mission-related

missions

Far-field

objects

has to be acquired

fly close

to detect

trajectory between the goals. Far-field planning map of the local terrain. However, the database adequate

Center

objects

crew, and interact with the guidance system to avoid in control, computer vision and image understanding.

planning

Research

is at best a very demanding

tools in order

Using

Sridhar

Ames

or man-made

of the rotorcraft

automation

of an automation

in high-threat

vegetation,

Navigation

different requires

processing

image further

range

sequences

47

tests were

The experience

gained

step before

flight. to objects

using real images

refinement

These

and is a necessary

curvilinear

to estimate

estimates

sequences.

requirements.

is very valuable

low-altitude

method

image

using

in a laboratory

to test its robustness. on the strategies

a sequence

of

set up and needs The feature

to limit the number

of

to

features(SridharandPhatak,1989).The extensionof thework reported require

the use of the extended

The research technologies estimation

reported

in this paper

for the automation algorithms

autonomous

navigation

Kalman

discussed

is part of an ongoing

of rotorcraft

low-altitude

are quite general

of vehicles.

here to curvilinear

flight may

filter.

In addition

efforts to investigate field-based techniques Sridhar, 1989; Kendall and Jacobi, 1989).

effort

flight.

and have

at NASA

The object

potential

algorithms,

estimation

to develop

detection

applications

to these feature-based

for the same range

Ames

and range

in robotics there

applications

and

are parallel (Menon

and

References Cheng,

V. H. L., and Sridhar, B., Considerations American Control Conference, Atlanta,

Kendall,

W. B., and Jacobi, W. J., Passive Electro-Optical Sensor Processing Avoidance, NASA Contractor Report, Contract NAS2-12774, 1989.

Menon,

P. K., and Sridhar, Navigation

Sridhar,

B., Passive

and Control

B., and Phatak,

for Automated Nap-Of-The-Earth GA, June 15-17, 1988.

Navigation

Conference,

A. V., Simulation

Using

Boston,

Image

Irradiance

MA, August

and Analysis

B., and Phatak, Using Japan,

A. V., Kalman

Imaging Sensors, July 1989.

Filter

Xlth IFAC

Based

of Image-Based

Range

Symposium

48

Estimation

on Automatic

for Helicopter

Tracking,

A/AA

1988

Obstacle

Guidance,

1989.

Rotorcraft Low-Altitude Flight, American Helicopter Society Applications of Rotorcraft, Atlanta, GA, April 4-6, 1988. Sridhar,

Flight,

Navigation Meeting

for Autonomous Control

System

for

on Automation

Navigation

in Aerospace,

Tsukuba,