Visual Localization in Highly Crowded Urban Environments

A. H. Abdul Hafez1, Manpreet Singh1, K Madhava Krishna1, and C.V. Jawahar1

Abstract— Visual localization in crowded dynamic environ-ments requires information about static and dynamic objects.This paper presents a robust method that learns useful featuresfrom multiple runs in highly crowded environments. Usefulfeatures are identified as distinctive ones that are also reliableto extract in diverse imaging conditions. Relative importanceof features is used to derive the weight of each feature. Thepopular bag-of-words model is used for image retrieval andlocalization, where query image is the current view of theenvironment and database contains the visual experience fromprevious runs. Based on the reliability, features are augmentedand eliminated over runs. This reduce the size of representation,and make it more reliable in crowded scenes. We tested theproposed method on data sets collected from highly crowdedin Indian urban outdoor settings. Experiments have shown thatwith the help of a small subset (10%) of the detected features,we can reliably localize. We achieve superior results in termsof an localization error even when more than 90% of the pixelsare occluded or dynamic.

I. INTRODUCTION

The localization problem tries to provide an answer to thequestion “Where am I?”. In other words, it is the process ofcomputing the current pose of robot in the environment [1],[2]. Appearance-based localization [2], [3], [4] is a variantof content-based image retrieval. The query image, in thecase of robotic localization, is the current view as seen bythe robot, and database is the past experience. Images arerepresented using local or global features. The utility ofglobal visual features was explored for mobile robot explo-ration, navigation, and localization [5], [6]. Local features [7]are computed at interest points on the image. However, thenumber of descriptors as well as the computations required tomatch explode with large databases. To handle the problemfor large databases, Sivic et al. [8] quantized the features intoa set of visual words, popularly known as the Bag-of-Wordstechnique. It is used along with popular techniques such asthe Inverted Index and Min-Hash [9] for fast matching andretrieval of images.

Localization in outdoor environment is difficult because,queries are often at different viewpoint, scale and illumina-tion from the previous visual experience [3]. In crowded andcluttered out door settings, the problem is further challeng-ing [10]. Dynamic objects could constitute most of the visibleregion and thus complicates the localization (see Fig. 1). Theresults are often erratic since the robot may not distinguishthe features from static and dynamic parts of the image.We are interested in designing a robust localization solutionin highly crowded urban environments. For this purpose,

1 International Institute of Information Technology, Gachibowli,Hyderabad-500032, India.

Fig. 1. Change in dynamic objects with time. The four images werecaptured at the same pose at different instances from a camera fixed tomoving vehicle.

Fig. 2. Random frames from [10] dataset (first row) and our dataset (secondrow) depicting extent of occlusion and large difference in useful visiblebackground.

collected multiple runs of data from crowded Indian roads.We aim at learning to localize better by finding out reliableand useful features.

Knopp et al. developed in [11] an automatic methodto detect and suppress confusing (useless) features. Themethod significantly improved the performance and reducedthe database size. Turcot and Lowe [12] reduce the databasesize by selecting a small portion of the total detected features.They call them useful features. The removed features are notuseful and not representative. The matching performance isas accurate as using the full set.

Cummins and Newman [4], [13] describe a probabilistic,called FAB-MAP, for recognizing places based on theirvisual appearance. Their algorithm is suitable for online loopclosure detection in mobile robotics. Milford and Wyeth [3]presented a new approach, is called SeqSLAM, for visuallocalization under different environment and illuminationconditions. Instead of obtaining the single most likely lo-cation given a current view, the system calculates the bestcandidate matching sequence. However, both methods pre-

Fig. 3. The first row shows visual features of images after one training run of environment. We tested Achar’s [10] formulation on these set of features.The second row shows the same frames with only useful features after fifth run.

sented above, i.e. FAB-MAP and SeqSLAM, are not tested inhighly crowded urban environments, they also neither use theconcept of useful features nor they identify dynamic objectin the scene.

Achar et al. [10] investigated the problem of localizationin urban environments, and conducted experiments to verifytheir methodology on roads in India. They propose a novelmethod to identify dynamic scene elements from the base runof the robot, and filter out features obtained from dynamicelements in order to facilitate robust localization of therobot. In contrast, a highly crowded environment containingdynamic elements, like standing pedestrians, movable objectsand parked vehicles is considered in our work as shownin Fig. 2. Such elements may not always be present at thesame pose in the environment, and hence can misguide thelocalization process of the robot.

We propose a method to learn elements that are “truly”static for a given scene and hence, improve localizationperformance. The learning of these useful features is doneincrementally over time. We assume that every time therobot runs through a certain spatial locality, it learns newfeatures with the possibility to discard part of the featureswhich are already learnt during previous runs. Over time thenumber of features converges to very small portion of thetotal features. This removal of non-useful features improvesmemory efficiency and computational time.

The remaining of this paper is organized as follows.Section II shows an overview of the proposed localizationmethod, while Section III focuses on learning useful featureswhen a new data is available from new run of the robotthrough the environment. Section IV explains the resultsfrom conducting a set of experiments support our proposedlocalization method. Finally, we conclude with some remarksand future work proposals.

II. QUALITATIVE VISUAL LOCALIZATION

Qualitative visual localization uses past experiences of therobot to determine a set of images in close neighbourhood ofits current pose. The motivation of our work arises throughobserving that a majority of extracted visual features belongsto the dynamic environment Fig. 3. Many recent works [10],

[11], [12] have argued that only a small percentage of theextracted features are useful. These however haven’t beentested in highly crowded environments, which is necessaryfor future autonomous systems. Our proposed frameworkis a localization methodology for these highly crowdedurban environments through multiple experiences. Ratherthan using available object detectors, we propose that theability to learn the useful subset of environment can beenhanced by traversing the same path multiple times.

The extraction of interest points, called features from theavailable images using suitable detectors is the first stepin visual localization. Many works have suggested the useof the available features in image retrieval but the Bag ofwords method [8] has been observed to be highly efficient.The method represents the images as a set of unorderedvisual words which are used to build an inverted indexwhich quantifies the occurence of each feature. A queryimage, captured at robot’s current pose is then quantizedto the exisiting vocabulary tree and mapped to a set ofvisual words. The weighted retrieval method, which identifiesdiscriminative features, returns a set of images that representthe current pose.

A. Bag of words model

The robot explores the environment during one traningrun and captures the frames referred as ground truth data.SIFT features [7] are extracted from the keyframes anda vocabulary tree of K branches and L depth containingKL leaf nodes is constructed using hierarchical k-meansclustering [2]. The inverted index stores for each visual wordthe occurrence of a feature and maintains its history throughweights . The occurrence of a feature has been quantifiedby tf-idf [8] in some earlier works, but in this paper we usethe weighted retrieval which assigns higher weight to highlydiscriminative features while lower weight to others.

B. Distinctive features

The distinctiveness of a given feature z with respect torobot pose x is a measure of the information that is added toour knowledge about the pose. Assume that our knowledgeabout the robot pose is represented by the distribution P (X),

while the amount of information given by the measurementis given by the P (Z). This can be interpreted as lookingfor features which appear in all images about some specificrobot pose, but rarely appear elsewhere. The concept of in-formation gain used in [2] captures this concept of distinctivefeatures.

Information gain I(X|Z) is a measure of how muchuncertainty is removed from a pose distribution P (X) givensome specific additional knowledge about features P (Z). Itis defined with respect to the entropy H(X) and conditionalentropy H(X|Z) of distributions P (X) and P (X|Y ). Fortwo random variables, X and Z, the entropy of X is givenby:

H(X) = −∑x∈X

P (X = x) logP (X = x), (1)

while the conditional entropy is

H(X|Z) = −∑z∈Z

P (Z = z)H(X|Z = z). (2)

Hence, the information gain is defined as

I(X|Z) = H(X)−H(X|Z). (3)

The information gain is always considered with respect toa specific robot pose xi and a specific feature zk. In otherwords, the distinctive weight wk of the kth feature zk in thevocabulary tree is computed as

wk = I(xi|zk) = H(xi)−H(xi|zk). (4)

More detailed computation of the distinctive weight, i.e.information gain, is available in [2], [10], [14], [15], wherethis concept is successfully used.

C. Querying

When the robot explores the environment again, it capturesa new set of frames, called query data. The extracted featuresfrom each query image are quantized to visual words usingthe available vocabulary tree and inverted index by usinggreedy N-best paths algorithm [2]. Since the vocabulary treewas built from ground truth data captured under differentconditions, a highly efficient method is required for both vo-cabulary tree construction and image retrieval. By assisgningdesciminative weights to the visual words corresponding toquery feature, a score is calculated for each ground truthimage by searching for visual words corresponding to theimage. To reduce the complexity of search, the position offeatures relative to the principal point of the camera is usedfor filtering irrelavant images [10].

The normalized score for the few relevant images iscomputed as:

Score(imgn) =

∑zk∈Zn∩Zq

Wk

|Zn|, (5)

where Zn is the set of SIFT descriptors in the nth key frameof base run, Zq is the set of SIFT descriptors in the queryimage, and Wk is the total weight of feature discussed laterin Eq. (12).

Fig. 4. Flowchart showing the gradual learning process of system. Theinput to the system are newly observed frames and output are useful features

The top N images based on the normalized score arereturned as the best matches for direct image retrieval. In caseof global localization, the feature correspondences betweenthe top matches and the query image are geometricallyverified using epipolar geometry and results are filtered basedon number of inliers.

The weighted bag of words retrieval method, howeversuffers from some disadvantages in crowded urban en-vironments. The inclusion of non-distinctive features likesky, lane markings and trees in the feature set results inproblems like perceptual aliasing which may lead to largelocalization errors. As discussed above, the vocabulary treealso provides inefficient output in such conditions which ne-cessiates stricter geometrical validation of matching features,thereby increasing retrieval run time. This paper addressesthe problem by identifying useful features of environmentthrough multiple experiences of robot which not only reducesthe size of vocabulary tree but also improves the performanceafter each experience.

III. LEARNING USEFUL FEATURES

Through multiple experiences, the objective is twofold:Firstly, to observe a set of new features which may be hiddenpreviously and secondly to identify the useful features fromprevious experiences using current observation. Thus, theexpectation is that through each new training run, the robotenhances its knowledge of the environment. Figure 4 depictsa block diagram of the learning process of our proposedmethod. From each available frame in the current run, SIFTkey points are extracted. To develop the relationship betweennewly observed features and the existing feature set, thebest matching frame from current run corresponding to eachframe from the first training run (henceforth called baserun) is determined. The available GPS data defines a smallneighbourhood in which the best frame is searched throughfeature matching followed by geometrical validation throughepipolar geometry. The repetitive features (useful features)are assigned reliability weights whereas the new features areaugmented to the feature set with some initial weight.

Factors such as illumination, clutter and weather condi-tions play a key role in the performance of visual localizationmethods. By traversing the same path multiple times, theeffect of these factors is minimized and the probability ofobserving the using features is increased. Every training runis classified as one of the following:

• Base run: The robot experiences the environment for thefirst time and unable to determine the useful features.

• Augmentation run: The knowledge of environment isenhanced through new experience and the useful fea-tures are learnt. Some newly observed features are alsoaugmented to the feature set.

• Elimination run: The useful features are retained basedon their reliability weights

A carefully designed methodology to use both augmentationand elimination run is the critical to the learning process.

A. Initial reliability weights in base run

The robot explores the environment for the first time andassignes equal weight to each feature. This weight, calledreliability weight indicates the usefullness of a feature inthe environment based on its ability to reoccur the neigh-bourhood of the same pose under different conditions overmultiple runs. Assuming M1 features are initially extractedand each kth feature is assigned reliability weight, w0

k = 1,the normalized weight is given as:

w0k =

1∑M1

k=1 w0k

=1

M1(6)

where∑M1

k=1 w0k = 1. Alongwith the reliability weights, the

robot also saves visual features and a GPS tag for each posewhich is used in future experiences.

B. Augmentation of features

As the robot explores the enviornment again under differ-ent conditions, it observes a set of features which may or maynot have been recorded previously. To develop a relationshipbetween these newly observed features and the exisitingfeatures, first the best visually matching frame from thecurrent run corresponding to every frame of the base run isdetermined. We realize that matching all the newly observedfeatures with exisiting may not be a realistic approach.So based on the available GPS data, a set of K imagesfrom current run are indentified which represent nearestneighbours of the image belonging to the base run. This isjustified since the useful features observed in an image of thebase run at a particular pose cannot be observed at an entirelydifferent pose in the current run. Thus, the problem of N1.Nt

computations is reduced to only N1.K computations andthe image with maximum number of geometrically validatedmatching features is labelled as the best matching frame.Here N1 and Nt are the number of images from base runand the current run respectively.

Any feature D1 from the selected best image is labelledas a match to a feature D2 of the base image if and only if:

d(D1, Di)

d(D1, D2)≤ T, (7)

where d(Di, Dj) is the Euclidean distance of descriptor Di

to descriptor Dj . This formula can be interpreted as thatthe Euclidean distance d(D1, D2) multiplied by a thresholdT is smaller than the distance of descriptor D1 to all other

descriptors Di in the image from base run [16]. SelectingT = 1.5 show best satisfaction.

The matching visual features represent the previously ob-served features, newly observed features as well as featuresbelonging to dynamic objects. These features are assignedthe visual stability weight given as:

wvs =FN

, (8)

where N is the number of visual features in the keyframe ofbase run, and F is the number of feature correspondencesbetween image of base run and its best matching image fromcurrent run.

The feature correspondences contain many negative re-sults, dynamic objects which are discarded by identifyingspatially consistent features through epipolar geometry. Thseare simply the inliers estimated by fitting a fundamentalmatrix using RANSAC algorithm and assigned the spatialconsistency weight as:

wsc =GN

, (9)

where N is the number of features of key frame of base run,G is the number of geometrically validated matches.

A large portion of the newly observed data consists of thefeatures belonging to the dynamic objects while a smallerpercentage, though useful belonging to to the static parthidden during the previous run. Despite the knowledge thatinclusion of these hidden static features would imply inclu-sion of large number of useless features as well, we augmentthe exisitng data because learning these hidden features hasbeen our objective throughout. The newly observed featuresare assigned a reliability weight, wk equal to the weightpossessed by the minimum weighted feature in the databaseduring the particular run.

It may be argued that a certain percentage of these hiddenuseful features, though distinctive may never reappear evenunder a different conditions, thus making it impossible forrobot to learn them. Such features which have a tendency tobe hidden for prolonged period of time are labelled as nonuseful features and treated similar to the dynamic features. Asimilar argument could be made for some dynamic objectswhich may always be present in the environment and maymislead the system into being considered as useful. For suchfeatures, we use the idea of the distinctiveness to negate theirrelative importance.

C. Enhancing the knowledge through elimination

The features belonging to dynamic objects and augmentedto the exisiting knowledge of robot pose the problem ofdecreasing the efficiency of localization process. However, itcan be analyzed that only the useful features would reoccurat the same pose under different conditions, i.e. gain higherreliability weight. Thus, the features with reliability weightsgreater than the minimum weighted feature are retained whilethe remaining are eliminated. To ensure that a visual featureis not discarded unfairly, we continue to augment featureset till third run. Though expensive, the idea is to provide

Fig. 5. Gradual learning process through base, augmentation and elimination run. Note the growth in number of features over frame 2 and 3 and finallyretention of only useful features in last frame.

new features sufficient support to prove theirusefullness. Thefeatures unable to prove their importance to the process arethen eliminated but at the same time, the newly observedfeatures in the third run are retained for future explorations.This ensures that the newly observed features during anytraining run are verified substantitally before elimination.Thus, after t runs, any kth useful feature will have areliability weight:

wtk = wt−1

k + wvs + wsc (10)

Then, it is normalized to produce the weight, wtk is given as:

wtk =

wtk∑Mt

k=1 wk

∈ (0, 1) (11)

where Mt is number of features after tth run Thus, the totalweight assigned to each feature of base run is given as:

Wk = w1.w2 (12)

where w1 = I(X;Y ) as in Eq. 4 and w2 = − 1log(wt

k)Here,

the total weight of a feature is directly related to its reliabilityand distinctiveness.

D. Amount of Learning

The robot learns the environment gradually with each runthrough augmentation and elimination of feature set. Sincethe data in the proposed work is collected over a shorterduration, the number of useful visual features is limitedin the environment. Hence, it is fair to assume that thesystem’s incremental learning ability will reduce with eachtraining run. Since 39.31% features have been classifiedas useful after seventh run (Table I), the system may notbe expected to learn further due to lack of static visualfeatures. It will be shown experimentally later that the meanerror also saturates after fifth run (Fig. 7). Rather, in somecases it increases slightly which is due to unintentionallylearning of reoccuring non useful features. This improvementin performance is also closely related to the accuracy of GPSdata. The performance of the system will never exceed theminimum error of GPS receiver.

IV. EXPERIMENTS AND RESULTS

A. Dataset creation by applying learning principle

The visual environment forming the dataset was capturedin the highly cluttered Koti Area of Hyderabad, India using aforward facing digital camera attached to a moving vehicle.The 640 x 480 resolution data containing 68700 frames was

recorded at 30 fps under varying conditions of traffic andillumination. The key frames were obtained by samplingthe training data at 10fps, the base run then having 2596frames. The motion blur and unpredictable motion of trafficadded to the uniqueness of collected data. A GPS receiverof permissible error was used to record the current positionof the vehicle and used later as ground truth data. Based onthe time stamp associated with the GPS information and thecaptured video, GPS tags were assigned to the extracted keyframes.

The total number of features increases linearly with eachtraining run and if allowed to continue would affect the com-putation time with no improvement in retrieval performance.By applying the proposed elimination concept, the numberof features was reduced by 90.99%, 84.63% and 80.94%for third, fifth and seventh run respectively which highlightsthe effectiveness of our approach in retaining only the smallsubset of useful features and eliminating the rest.

B. Testing

Training video from eighth run of the same environmentwas sampled at 10 frames per second to obtain 3734 keyframes. Though there was a large variation in number ofuseful features with each training run (Table I)the size ofthe vocabulary tree was kept constant at 537K without anyobserved degradation in performance. As already explainedin II-C, the inverted index of the quantized visual wordswas searched for matches and the score was assigned toeach database frame. The top 10 results based on the scorewere returned as best matches and labelled as direct retrievalresults. The matched features in this set of 10 top resultswere geometrically verified to remove the mismatches andtop results were again obtained based on the number ofgeometrically validated features. GPS ground truth data wasused to check localization performance by measuring meanerror for both direct retrieval and global localization. Thereturned results were deemed to be correct if the localizationerror was less than 7.5m. The proposed formulation wasimplemented on three datasets. For all the tests, the queryimages were chosen randomly assuming independent andidentically distributed samples.

1) Test 1: The first 500 key frames of all the eight runscovering (approx) one-fifth of the total path were chosen.The key frames from the first seven runs were used to builddatabase of useful features. A random 200 frames extractedfrom the first 500 frames of eighth run were used as query.

TABLE IVARIATION IN NUMBER OF VISUAL FEATURES WITH EACH TRAINING RUN. THE NUMBER OF USEFUL FEATURES RETAINED BY SYSTEM AFTER

ELIMINATION ARE ALSO SHOWN. THE DRASTIC REDUCTION IN NUMBER OF FEATURES IS INDICATIVE OF THE AMOUNT OF CLUTTER PRESENT

Training Run 1 2 3 3-E 4 5 5-E 6 7 7-ENumber of features 2.90M 5.53M 8.15M 734k 3.38M 6.02M 925k 3.52M 6.01M 1.14M

Fig. 6. Sample query frames from eighth training run.The repeatingdynamic elements, pedestrians and the clogged roads make the localizationprocess difficult.

Fig. 7. Variation of mean localization error for both direct retrieval (solidlines) and global localization (dashed lines) corresponding to the three testsis shown. The performance can be observed getting better with increase innumber of runs. However, a slight increase in mean error is observed forseventh run which can be attributed to unintentional learning of dynamicfeatures by the system. (Green=Test 1, Blue=Test 2, Red= Test 3)

2) Test 2: To check for accuracy over larger number offrames, the database of useful features was built using all thekeyframes from the first seven training runs. After buildingthe vocabulary tree, a random 1000 key frames were chosenfrom the eighth run and queried for both direct retrieval andglobal localization.

3) Test 3: To check the scalability of our proposed workand the effectiveness of larger vocabulary tree, database ofuseful features was built as in test 2 from the first seven runsbut the full eighth run, i.e. 3734 key frames covering the fullpath were used as query.

C. Discussion

Despite occlusion of useful features, the returned resultshave shown good visual performance. As can be seen fromadjoining Fig. 8, good performance was achieved after fifthtraining run with only 30% visual features whereas tests afterfirst run with all the features based on the work of [10] failedto give desired results. The failure was due to large occlusionof useful features as well as similarity in appearance ofscenes. In our case, even the total 30% features containeda major portion belonging to sky and roads. So, the actualavailable useful features were far less, on an average 10%.

Fig. 8. The first row shows the query frames. The second row shows theretrieved results using the formulation of [10] whereas the third row showsthe retrieved results through the proposed method after fifth run using only10% useful features

Over the full eighth run, our formulation continued to returnresults similar in appearance to the query and belonging tothe same pose.

The quantitative analysis (Table II) in terms of distancemeasure has shown remarkable improvement. The directretrieval and global localization results showed an improve-ment of 60.72% and 43.12% respectively after seventh runfor Test 3 (Fig. 7). The reduction in variance is much largerwith 95.53% and 98.46% respectively for both the cases.Considering the inherent GPS error and usage of only 10%useful features, the currently achieved results can be termedas excellent and support our approach to gradually learn theuseful features through multiple experiences.

12 frames from a total of 3734 queries returned extremelylarge errors (greater than 20m) that can be observed bothvisually (Fig. 9) and quantitatively (Fig. 10). The match-ing features in these queries belong to the non distinctiveelements like trees, lane markings and sky which beingrepetitive, are incorrectly classified as useful because ofthe weights. This error can be attributed to the inability ofAchar’s method to effectively assign low weights to non-distinctive features. Our argument is aided by the fact thatno matching feature in these frames belonged to the dynamicclutter, implying that our proposed method is successful.

V. CONCLUSION AND FUTURE WORK

The basic objective of the paper to achieve good localiza-tion performance using only the set of useful features thatare not more than 10% of the total feature excluding the skyand road features, has been surpassed with extremely positiveresults and large improvement in both visual appearance aswell as quantitative measurement. The learning principle hasbeen effectively applied to the system and the challengingtask of searching useful features amongst clutter has been

Fig. 10. The first row shows the mean localization error comparison for direct retrieval between Run 1 and Run 7 corresponding to Test 3. The secondrow compares the same for global localization. The few frames with large errors even after seven runs are shown in Fig. 9

Fig. 9. Shown are the matching features of the frames returning large errors.Most of these matching features correspond to non distinctive elements liketrees and lane markings

reduced to a simple cyclic process of augmentation andelimination. At the same time, we have observed that theunintentional learning of non-useful features by the systemneeds to be avoided by carefully monitoring its changein performance after every run. Though the performanceof system has been excellent over the majority of queryframes, large error has been observed for a small percentageof frames (0.003%) due to failure of distinctiveness. Thisis a concern that needs to be addressed by designing arobust mathematical framework for recognition of distinctivefeatures in such densely cluttered environments.

REFERENCES

[1] J. Leonard and H. Durrant-Whyte, “Simultaneous map building andlocalization for an autonomous mobile robot,” in IROS Workshop,1991, pp. 1442–1447.

[2] G. Schindler, M. Brown, and R. S. Szeliski, “City-scale locationrecognition,” in CVPR, 2007, pp. 1–7.

[3] M. Milford and G. F. Wyeth, “Seqslam: Visual route-based navigationfor sunny summer days and stormy winter nights,” in ICRA, 2012, pp.1643–1649.

[4] M. Cummins and P. Newman, “FAB-MAP: Probabilistic Localizationand Mapping in the Space of Appearance,” IJRR, vol. 27, no. 6, pp.647–665, 2008.

TABLE IITHE QUANTITATIVE PERFORMANCE IS SHOWN FOR BOTH DIRECT IMAGE

RETRIEVAL AND GLOBAL LOCALIZATION. THE PARAMETERS INCLUDE

MEAN ERROR (M), PERCENTAGE ERROR (P) (ERROR LARGER THAN THE

THRESHOLD OF 7.5M) AND VARIANCE (V). HERE Ti INDICATES THE

QUERY DATASET USED WHILE Rj IS THE RUN NUMBER WHERE

i = 1, 2, 3 AND j = 1, 3, 5, 7

Direct Retrieval Global LocalizationM (m) P (%) V M (m) P (%) V

T1R1 9.47 19.50 305.28 6.28 8.50 229.44T1R3 1.97 0.50 1.88 1.86 0.00 0.39T1R5 1.95 1.00 11.73 1.75 0.00 0.42T1R7 2.27 1.50 20.79 1.74 0.00 0.68T2R1 6.48 4.60 640.90 5.12 3.90 355.09T2R3 6.50 6.40 419.60 4.22 3.40 142.92T2R5 3.70 2.20 104.53 3.12 1.80 7.08T2R7 4.09 3.10 81.36 3.05 1.40 8.70T3R1 9.70 8.16 879.77 5.82 4.65 333.14T3R3 8.48 8.67 545.31 5.50 4.49 386.49T3R5 6.26 5.32 286.71 3.59 2.03 25.56T3R7 3.81 2.00 39.24 3.31 0.88 5.13

[5] D. Santosh, S. Achar, and C. V. Jawahar, “Autonomous image-basedexploration for mobile robot navigation,” in ICRA, 2008, pp. 2717–2722.

[6] C. Zhou, Y. Wei, and T. Tan, “Mobile robot self-localization basedon global visual appearance features,” in ICRA, September 2003, pp.1271–1276.

[7] D. G. Lowe, “Distinctive image features from scale-invariant key-points,” IJCV, vol. 60, pp. 91–110, 2004.

[8] J. Sivic and A. Zisserman, “Video Google: A text retrieval approachto object matching in videos,” in ICCV, 2003, pp. 1470–1477 vol.2.

[9] O. Chum, J. Philbin, and A. Zisserman, “Near duplicate imagedetection: min-hash and tf-idf weighting,” in BMVC, 2008.

[10] S. Achar, C. V. Jawahar, and K. M. Krishna., “Large scale visuallocalization in urban environments,” in ICRA, 2011, pp. 5642–5648.

[11] J. Knopp, J. Sivic, and T. Pajdla, “Avoiding confusing features in placerecognition,” in ECCV, 2010.

[12] P. Turcot and D. Lowe, “Better matching with fewer features: Theselection of useful features in large database recognition problems,”in ICCV Workshop, October 2009, pp. 2109–2116.

[13] M. Cummins and P. Newman, “Appearance-only slam at large scalewith fab-map 2.0,” IJRR, vol. 30, no. 9, pp. 1100–1123, August 2011.

[14] T. Vidal-calleja and J. Andrade-cetto, “Active control for single cameraslam,” in ICRA, 2006, pp. 1930–1936.

[15] A. Dame and E. Marchand, “Using mutual information for appearance-based visual path following,” in Robotics and Autonomous Systems,2013.

[16] A. Vedaldi and B. Fulkerson, “VLFeat: An open and portable libraryof computer vision algorithms,” 2008.