Visual Indoor Localization with a Floor-Plan Map

Transcription

1 Visual Indoor Localizaion wih a Floor-Plan Map Hang Chu Dep. of ECE Cornell Universiy Ihaca, NY hc772@cornell.edu Absrac In his repor, a indoor localizaion mehod is presened. The mehod akes firsperson view video clip as inpu and uses a 2D floor-plan map as prior knowledge. Moivaion, mehodology, elemenary experimenal resuls, and furher exensions are repored and discussed. This repor also serves as a brief research summary for CS Moivaion 1.1 Indoor localizaion The problem of indoor localizaion arises as he blocking of building srucure usually causes deviaion o GPS sysem. Indoor localizaion has los of poenial applicaions and is under exensive researching by various communiies. Two main approaches have been sudied [1]: On one hand, wireless hospos such as wifi accessing poins are used, heir unique IDs and signals are recorded and analyzed o consruc an indoor signal srengh map and hus enables room-wise localizaion; On he oher hand, more complex sensors such as laser scanner, camera and IMU (inerial measuremen uni) are used solely or joinly o scan he indoor room srucure and localize in he same ime, his echnique is known as SLAM (simulaneous localizaion and mapping). Boh of he wo approaches have various well-developed, sophisicaed implemenaions. However, a major issue exiss in pracical use. For wireless ID and SLAM approaches, he consrucion of map (signal srengh map for he former, laser range map or visual word map for he laer) is ineviable. Tha implies he necessiy of a horough walk-hrough of every building before puing he localizaion sysem ino service. To address his issue, a 2D floor-plan map, which is widely available and easily accessible, provides a perfec soluion. 1.2 Localizaion wih maps Being he mos universal mehod when i comes for a person o find a locaion, map-reading is cerainly an useful abiliy, which is rue for boh human and machines. For his projec, wo specific map-relaed previous work inspired he major framework GPS refinemen wih an image and map In his work, a mehod for reducing GPS error in urban environmen is presened. The refinemen is based on firsly he analysis of building srucural lines (see Figure 1) and camera pose (Figure 2), and hen a reference o he region map (Figure 3). This mehod is proved o produce beer locaion han GPS generally does (Figure 4), alone wih a saisfacory orienaion esimaion. Though he approach in his work is fairly simple and inuiive, i sill reveals he poenial of uilizing a map, o be more specific clues of building srucure embedded in he map, o he ask of 1

2 Figure 1: Verical corner edges of building Figure 2: Compuing camera il angle Figure 3: Localizaion by referring he map Figure 4: RMSE in locaion localizaion. Similar o he oudoor siuaion, well-mainained indoor maps could be also useful. This is he work I concenraed on in he firs half of he semeser, i is no ye published [2] Vehicle visual localizaion wih a map In he work of Brubaker e al. [3], a novel approach for vehicle localizaion is proposed. The only required sensor is a self-moion odomery sensor (in his work a dual-vision camera ha is mouned on he car roof), and he only required pre-sage daabase is a road map exraced from any available online ciy map. To do localizaion, moion rajecory is esimaed from he odomery inpu, hen localizaion soluions are proposed based on he similariy of moion rajecory and road map srucures. This is shown in Figure 5, localizaion accuracy increases as ime passes and more srucural informaion is provided by he moion rajecory. Figure 5: Brief illusraion of localizaion echnique in [3] 2

3 A similar sraegy could be adoped for indoor localizaion. However, here exiss wo main differences beween wo problems. Firs, he indoor equivalen o oudoor road inersecions are doors, bu vehicles can only move on roads from one inersecion o anoher, while a human can move hrough any wished pah from one door o anoher. This implies ha he soluion space of indoor localizaion is much bigger han oudoor vehicle localizaion. Second, vehicles move seadily and he odomery sensor produces daa wih only 2 degree of freedom (assuming fla ground everywhere). This is no he case for pedesrian moion, wheher using IMU (for example a cellphone IMU or shoe-mouned IMUs [4]) or using visual inpu (for example a Google glass or a wais-mouned camera [5]), he exraced moion rajecory is in 3D. This implies more noisy odomery inpu, hus more careful measuremen calibraion and more noise-robus localizaion algorihm are needed. 2 Approach 2.1 Localizaion wih only kinemaics As a classic seing of indoor robo SLAM [6], he sensing sysem measures boh odomery (referring o laser range measuremens or visual feaure maching wih buil visual-word-map here, migh cause a lile bi confusion wih visual-odomery as hey coincidenally chose he same word) and kinemaics (measuring he ego-moion). In his work we wan o skip he map-building par and only use daily-life sensors, so odomery is no available. The problem now becomes finding locaion using only kinemaics (and of course a 2D floor-plan map). Le s firs no concern abou noise in he measuremen, which means perfec moion rajecory can be recovered from kinemaics. Then finding he locaion simply becomes finding a locaion o place ha rajecory on he map wihou hiing a wall. As shown in Figure 6, given a sufficien long rajecory and no a Manhaan-like map, saisfacory localizaion can be found. Figure 6: Localizaion wih perfec rajecory. Blue curve shows he rajecory (in is ground-ruh placemen), red region shows possible placemens of he sar poin of rajecory However, perfec measuremen is no possible. To deal wih he noisy-inpu siuaion, he previous mehod can be modified ino a paricle-filer (also known as Mone Carlo Localizaion) syle. Iniialize locaion saes x (i) 0 using an uniform disribuion, wih equal weighs w (i) 0. For each sep, measure ego-moion in 2D as m. Sample x (i) Updae saes x (i) Se w (i) according o x (i) 1 wih weighs w(i) 1. as 0 if x (i) 2.2 Moion rajecory esimaion p(x m, x). locaes in wall region, C oherwise. Normalize w (i). There are muliple opions for measuring he moion rajecory of a walking person, he mos accurae one is using a camera. The echnique for compuing relaive moions beween frames is known as visual odomery [7]. As he key for solving i is essenially an epipolar geomery problem, visual odomery can be seen as a special case of general srucure-from-moion. I sar wih he welldeveloped normalized 8-poin RANSAC [8] and 5-poin RANSAC algorihm [9]. Figure 8 shows 3

4 Figure 7: Localizaion wih noisy rajecory. Blue curve shows he rajecory (in is ground-ruh placemen), red dos show locaion esimaions of paricle filer of he curren locaion sample frames of a esing video sequence aken in he Ward Lab, Figure 9 shows he resuls of menioned algorihms. Boh algorihms are unable o recover a highly accurae rajecory, bu hey boh succeed in capuring he overall rend and fragmenal lile moions. Figure 8: Sample frames of a 9-second video sequence of Ward Lab Figure 9: Trajecories of normalized 8-poin RANSAC (lef) and 5-poin RANSAC (righ), showing only he X-Z plane, assuming he firs frame looking a posiive-z direcion. Red needles show he esimaed camera orienaion. 3 Fuure works As his is sill an ongoing research projec, only elemenary framework and examples are shown in he previous secion. This secion we will focus on possible improvemens and exensions ha can be added o he framework. 3.1 More reliable and faser localizaion The main problem in localizaion now is ha i does no consider he orienaion of rajecory placemen. If we incorporae he iniial orienaion, he soluion space becomes much larger. In classic 4

5 robo localizaion, his is no a problem because laser range measuremen and visual-feaure maching significanly reduces he possible soluion. In our siuaion as his no available, he only soluion is o increase paricle numbers so i covers he whole iniial soluion space well. Tha indicaes a radeoff beween efficiency and reliabiliy of he algorihm: more paricles gives beer coverage of he soluion space bu also makes he algorihm slower. A possible soluion is o adapively change paricle numbers. Paricles form clusers as ime passes, by adding a cluserness measuremen, he algorihm is able o sar wih a large number of paricles and reduce he number while i is running. Besides, mixure-mone-carlo process also offers a possible soluion. 3.2 Obaining beer rajecory measuremen Improving accuracy of visual odomery Three echniques could be used for producing more accurae moion rajecory. Firs is applying a filering process such as he exended-kalman-filer, his migh reduce he influence of noise while requires lile compuaional resource. Second is applying a pose-graph opimizaion [10], which measures relaive moion of any pairs of frames in a sliding window. Third is applying bundle adjusmen, where he opimizaion focus on visual feaures raher han jus on a frame-level. The las o echniques are boh essenially a non-linear leas square problem and are proved solvable efficienly, heir drawback is ha hey require much more feaure maching operaions, which would increase compuaional resource demand significanly Oher problems in visual odomery Besides improve he accuracy of visual odomery, here exis wo oher problems, naurally due o he specific seing of his ask. The firs problem is how o deal wih he dimension reducion from a 3D rajecory o a 2D one, o do his precisely he esimaion of graviy direcion will be needed. A possible soluion is o invesigae srucural lines of walls and doors, which is normally verical and helpful for esimaing graviy direcion. The second problem is reduce he inerference of oher moving objec o moion esimaion, hough he RANSAC process already handles his issue, i migh no be enough for an indoor scenario where a walking person is more likely o generae feaure poins han walls or saionary furniure. This is also a problem ha needs o be solved for pracical use of he enire framework. [1] Oliver Woodman & and Rober Harle. (2008) Pedesrian localisaion for indoor environmens. Inernaional Conference on Ubiquious Compuing. [2] Hang Chu, Andrew Gallagher & Tsuhan Chen. (2013) Efficien GPS refinemen and camera orienaion esimaion from a single image and a 2D map. CVPR submission. [3] Marcus A. Brubaker, Andreas Geiger & Raquel Urasun. (2013) Los! leveraging he crowd for probabilisic visual self-localizaion. CVPR. [4] John-Olof Nilsson, Isaac Skog, Peer Handel, & K.V.S. Hari. (2012). Foo-mouned INS for everybody-an open-source embedded implemenaion. Posiion Locaion and Navigaion Symposium. [5] Jwu-Sheng Hu, Chin-Yuan Tseng, Ming-Yuan Chen & Kuan-Chun Sun. (2013) IMU-assised monocular visual odomery including he human walking model for wearable applicaions. ICRA. [6] Sebasian Thrun, Dieer Fox, Wolfram Burgard & Frank Dellaer. (2001) Robus Mone Carlo localizaion for mobile robos. Arificial Inelligence. [7] Davide Scaramuzza, & Friedrich Fraundorfer. (2011) Visual odomery uorial. Roboics and Auomaion Magazine. [8] Richard Harley, & Andrew Zisserman. (2000) Muliple view geomery in compuer vision Cambridge. [9] David Niser. (2004) An efficien soluion o he five-poin relaive pose problem. TPAMI. [10] Edwin Olson, John Leonard & Seh Teller. (2006) Fas ieraive alignmen of pose graphs wih poor iniial esimaes. ICRA. 5