Qualitative route scene description using ... - Computer Science - iupui

Qualitative Route Scene Description Using Autonomous Landmark Detection diang Yu Zhengi., Matthew Barfh nnd Sahuro ‘I’suji

r)ePartnient of Control Engineering, Osaka University Toyonaka. Osaka 56-0, Japan

Abstract This work introdurrs an approach to build a quatitative description of scemes dong a roate, which is used in routc recognition by a mobile robot. The description consists of a series of Landmarks eutonomowly selected by the robot from a Panurantic View. which has been generated as a visual memory of scenes along routes. The h i e idea lo bridge the quantitative panoramic view to qualitative landmarks is t o examine t,he ‘dist$xLi.tivenoss’ of patterns in the image md select landmarks from uniqw, patterus that are remarkable by which to navigate.

1. Introduction 1.1. Background Long distance m b d e robot navigation is faced with the t w o major problems of haw to memorise scenes along a route and haw to recognize the route when referring to these scrmes. In order to investigate these topi-, w e have considered the fdlowing strategy. First, a robot constructs an internal representation of a r0ut.e from specific scenes during a trial move guided by a human. When t h e s m route is subsequently piirsued agaiu hy the robot itsew, it Lucetes and orients itself based on the memorised sceies[‘l. A Panoramic Repremutation usrxl for presenting and retrievhg vioxwly propost& 3;. Briefly, this repre from slices d continuous images taken by a carriera om a inobile robot, The resuiting Panoramic View (PV)[’r r o d e planning, a ~ n e pon the qualitative b e l tends to be necessary, This paper describes a method of rw1istructiu.g such a qualitative representation from the panoramic representation. Figure Z givos the basic concept on l i m to abstract the qualitative repwse-tatirm. Specific scene*. dermted tis ‘landmarks‘, are extracted F r o m the DV. The control parameters from the robot control unit are atso taken in order to yield symnbdic inStrUCtkJll.*that are used to drive the robot (such as turning at corners), The generated paths in the panoramic represcutation then become a topological netwol.k, resulting in a qualitative map. The advantages of a qualitative descfiption lie in two major aspects: (1) It is small and compact, and its eccess is straightforward. (2) It is rohiwk to tFre srnall changes in parameter values and view points and is thus rclinbfe darhg rvcognition.

consider how a human memoxizes a route. Accarding to cognitive science, a k y function of the visua1 memory of humans i s sekctivit$6j. A human only renienihers the most &tinct scenes when subjected to a large amount of visual infomation. Whai a human traverses a new route, what fie most likely mem~ri~ are e ~the mest ’striking’ scenes in the range of observation, and he will probably describe the route by these scenes. In spite of the multitude of visual detaixs in the area between the strikuag scenes (which ran Iwinforce the memory during fin-ther navigation), the Erst set of sr?sponses to the route wili stin he the scenes that are most renrorkable to n human. Usually, we refer to these distinctive srenerr as Lanclmarks. One of t€m more challenging tasks for a mobile mbot ls the autonomous extraction of landmarks &ng a route. Up to now, such tasks have been done primarily with the assistance of human operators identifying objects in specific environments[7’j, When such a priori knowbdge i s uwd for landmark selection, route recognition is a goal-driven event. However, when no a p i m i knowledge can be given about the landmarks, route recognition dgoritlims must rety on data-driven methods. Ours is primarily a dah-driven ruethod. Skce a mobile robot should be able tu move in various environments, our lauchnarks are not defined as m y specific type of object. Xn o w method. bCJttOiIl-Up algorithms operate tu autanomnously extract landmarks from the scenes dnrbig robot’s learning phase along the route. T h critcu-ia fur landmark selection axe based on the frdlwwing itleas: 1. The scenes extracted rjs landmarks shrmld be remarkable in size a d explicit in contrast or color so that they will not btx missed in recognition. 2. T h e scenes extracted as landmarks should be unique and distinctive either in properties or in structure, compared with other scenes in certain ranhws, We measwe haw unique a s a m e is froin its Distinctive Rarige H which no other similar scene appears. We describe a scene not ordy on i t s own attributes (i.e. color. shape, or structure) but also on its spatiat rdaGonStrip with ther swnee. Our landmarks are selected as sections of a PV which contain distinct and unique smnes. Sinro the panoramic representation irrcurporates the majority of infurmation in the scenes t-isible ahmg the route, it is well suited fur eompnrisons betwmn extracted pattexns when determining distiuctivmiess. Farthermore, as a gad, the landmarkbased qualitative representation should nearly maintain the equivalent infoiwsation iiermssaxy for route recvgnition as the PV.

2. Basis for Landmark Detection 2 , i Fanuramie Representatiun 1% b r i e describe how a panarmiic view is formed. A camera on a mobile robot moves along a smuotb curve on a horiaontal plane, with its optical axis directed sideways from the dircctian of nidion. The scent%dong the route are &Jserved through a vertical slit. T h i s is redzed by capturing data a t the? slit from each image taken cunthiuowly. and pasting them consecutively at the g r i t i o n along the trajectory as fignrc 3 depictd3j. The scenes dwig the route are therefore prr>je.rtwltowards e vertical surface determined by the trajectory of the camera. The PV has a wide field so fbat it is useful in presenthig a global relathuship of the scenes. The amoaut