SeqSLAM++: View-based Robot Localization and Navigation
Shuji Oishia,∗, Yohei Inoueb , Jun Miurab , Shota Tanakab
a Smart Mobility Research Team, Robot Innovation Research Center, Department of Information Technology and Human Factors, National Institute of Advanced

Industrial Science and Technology (AIST)b Department of Computer Science and Engineering, Toyohashi University of Technology

Abstract
This paper presents a new approach to view-based localization and navigation in outdoor environments, which are indispensable
functions for mobile robots. Several approaches have been proposed for autonomous navigation. GPS-based systems are widelyused especially in the case of automobiles, however, they can be unreliable or non-operational near tall buildings. Localization witha precise 3D digital map of the target environment also enables mobile robots equipped with range sensors to estimate accurate
poses, but maintaining a large-scale outdoor map is often costly. We have therefore developed a novel view-based localization
method SeqSLAM++ by extending the conventional SeqSLAM in order not only to robustly estimate the robot position comparing
image sequences but also to cope with changes in a robot’s heading and speed as well as view changes using wide-angle images
and a Markov localization scheme. According to the direction to move provided by the SeqSLAM++, the local-level path planner
navigates the robot by setting subgoals repeatedly considering the structure of the surrounding environment using a 3D LiDAR. The
entire navigation system has been implemented in the ROS framework, and the eﬀectiveness and accuracy of the proposed method
was evaluated through oﬀ-line/on-line navigation experiments.
Keywords: SeqSLAM, View-based localization, Navigation, Mobile robot

1. Introduction

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The mobile service robot is an emerging application area inrobotics. Such a robot is expected to provide various service 30
tasks like attending, guiding, and searching. One of the necessary functions of mobile service robots is navigation, whichmakes it possible for a robot to move from one place to anotherautonomously. Since outdoor environments are an importantpart of human activity, mobile service robots should be able to 35
navigate themselves in the outdoors.
Outdoor navigation can be divided into two levels. The
global level deals with localization and subgoal selection, while
the local level deals with safe navigation in a local area. Thisis an analogy to a navigated car driving; a car navigation sys- 40
tem tells a driver where the car is and which way to go, and thedriver is responsible for safe driving, including following traﬃcrules and avoiding possible collisions.
Several approaches are possible for outdoor global localization. GPS-based systems are often used [18, 19], but can be 45
unreliable or non-operational near tall buildings. A precise digital map of the environment is also required in this approach.
A map-based approach is also popular in the SLAM context[20, 21, 22], however making a large-scale outdoor map is often
costly. In this paper, we seek a simpler way, that is, view-based 50
localization [1, 2, 3, 4].
In a typical view-based localization, a route to follow isrepresented by a sequence of views and a view-matching pro∗ Corresponding author

55

cedure is employed for determining the robot’s current location. One of the issues when applying this approach to outdoor scenes is robustness to view changes due to changes of,for example, weather, seasons, and time of day. Among various approaches to resolving the issue, we adopt the SeqSLAM
method [9], which achieves a high robustness even under extreme view changes by using image sequence matching. Although this method shows a good performance for image sequences taken from a vehicle, it is not always directly applicable to mobile robot navigation.
We therefore developed SeqSLAM++ by extending the conventional SeqSLAM for a new view-based navigation framework for mobile robots. The major contribution of this paper
is to generalize the SeqSLAM so that it can handle frequentchanges of a robot’s heading and speed due to its motion, andprovide a direction to move at a certain interval to follow thetraining trajectory. As shown in Fig. 1, the proposed SeqSLAM++ autonomously navigates the mobile robot to the destination according to the result of the robust image sequence
matching. Combined with local mapping and path planning capabilities, the SeqSLAM++ generates a local subgoal based on
the estimated direction to move and makes the mobile robot go
to the destination tracing each subgoal.
The rest of the paper is organized as follows. Sec. 2 presents
related work on view-based localization. Sec. 3 describes thedetail of the proposed SeqSLAM++ for global-level mobile
robot localization and navigation. Sec. 4 describes a local navigation strategy including local mapping, subgoal selection, andpath planning. Sec. 5 shows the experimental results of oﬀ-line

Email address: shuji.oishi@aist.go.jp (Shuji Oishi)
Preprint submitted to Journal of LATEX Templates

November 14, 2018

(a) Mobile robot navigating itself to a destination.

(b) Input image and selected moving direction.

(c) Estimated location (The best matched image in the training sequence).

(d) Local path planning along with the direction to move.

Figure 1: Autonomous navigation with the proposed SeqSLAM++.

performance validation, on-line navigation, and trajectory evaluation. Sec. 6 concludes the paper and discusses future work.
2. Related Work

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3. View-based Localization by SeqSLAM and Its Improvements
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View-based localization is to find the location of a robot ora vehicle by matching the current view with those in a set ofviews that has been previously obtained. The Robustness ofmatching to large view changes is, therefore, the key to reliable 95
localization, and many approaches have been proposed. Suchan image-to-image matching also appears in the context of loop
closure detection for SLAM [23, 24] or in visual place recognition [25].
Local image descriptors such as SIFT [26] and SURF [27]100
are sometimes used for characterizing an image with, for example, the Bag-of-Visual-Words model [28]. This visual wordsbased approach is popular in outdoor localization [29, 4]. FABMap [3, 30] improves this approach by constructing and using
a generative model of visual words based on their probabilistic
relationships. Local image descriptors are, however, sometimes
weak to large changes of weather and seasons [4].
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Global image features such as GIST are also used for place
recognition [31]. Combination of GIST with saliency-based
landmarks is also proposed [32, 33] for localization in a topological map. However, since global image features still cannot
cope with large view changes, an image database must be con110
structed by training under various lighting conditions [5].
Enhancing direct image-to-image matching is another approach, examples of which include a robust template matching[8], an SVM learning of objects with varying views [7, 6], andan illumination invariant image transformation [34]. Among
those, the SeqSLAM method [9] achieves highly robust localization based on a robust image sequence matching. We extend
this SeqSLAM for our view-based robot localization system.
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3.1. SeqSLAM
SeqSLAM [9] is a view-based localization method which
compares a reference image sequence with an input sequence
for robust matching. It achieves highly accurate visual recognition by calculating the similarity between the reference andthe input image sequences to estimate the best matching location instead of single image comparison. For robust estimationin an extreme lighting condition between training and testingtime, it applies local contrast enhancement as follows.
Let D be the vector of the diﬀerences between an input image and the images in the reference sequence, which is considered to cover the possible range of reference images for the
input image. Each element Di in D is normalized by:()
D̂i = Di − D̄l /σl ,(1)where Dl and σl are the mean and the standard deviation of D.
By this enhancement, even if an input image is largely diﬀerent from the reference images and all of the diﬀerence values
become large due to significant illumination change, the diﬀerence for the true correspondence is expected to be suﬃciently
small compared to the others.
These enhanced vectors are compiled for d s +1 frames into amatrix M which has the reference and the input image sequencein the row and the column, respectively:[]
M = D̂T −ds , D̂T −ds +1 , . . . , D̂T(2)
An example matrix is shown in Fig. 4.
Then, assuming a constant velocity during the sequence, aline is searched for which minimizes the following total diﬀer-

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ence S :
S

=

T
∑

Dtk ,

(3)

s + V(d s − T + t),(4)

t=T −d s

k

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=

where V is the gradient of the line (or a relative velocity in input
and reference acquisition) and k is the index of the corresponding image in the reference sequence for the input image at timet.
SeqSLAM exhibited great performances against drastic view
changes, at least for road sequence images. There are, however,room for improvements when applied to mobile robot navigation. The following subsections explain our improvements.

(a) Example scenes.

(b) Image normalization results by SeqSLAM.

3.2. SeqSLAM++ : Extension of the SeqSLAM for mobile robot
navigation
3.2.1. Robust image matching and motion determination
The original SeqSLAM uses intensity values normalized
within a small window as the feature for image matching. The
method is simple and fast, however, is not robust to image deformations. In addition, it is not able to deal with large view direction changes due to the robot’s motion. We therefore adopt
two improvements: HOG feature matching and the use of awide angle camera.

(c) Extracted HOG features.

Figure 2: Feature representation for image matching.

HOG feature matching: HOG feature [10] is a histogramof edges in a local region and suitable for representing shape information. The training images have 90◦ FOV (Field Of View),
and the cell size for calculating HOG is determined so that eachcell has 5◦ FOV. In addition, the number of bins is set as 9 andthe block consists of 2 × 2 cells.
Fig. 2 shows an example of SeqSLAM’s normalization and
HOG calculation. Compared with the normalization method,
HOG can extract an appearance feature invariant to lighting
conditions and small deformations thanks to the discretized gra-165
dient representation and block normalization. The dissimilarity
between images is obtained by calculating a normal SAD (Sumof Absolute Diﬀerences) in the proposed SeqSLAM++.
Moving direction determination to deal with a varietyof robot motion: Mobile robots changes their moving direc-170
tions frequently not only for moving toward a destination but
also when avoiding collisions with people and obstacles. Since
each image in a view sequence captures a scene in a specificdirection, it is very likely to have a diﬀerent orientation during navigation, thereby degrading the view-based localization175
performance.
Morita and Miura [11] used an omnidirectional camera tocope with this problem. We also take a similar approach using wide-angle images that have about 180◦ horizontal FOV
as input images. A training image is scanned horizontally on180
the wide-angle input image within ±45◦ range at 5◦ interval,
and choose the minimum distance position, which is then used
for determining the subgoal direction (i.e., the direction for the
robot to move). Fig. 3 shows an example of the direction determination.
1853(a) Training image.

(b) Input image with the selected direction (red box).

Figure 3: Selection of moving direction.

3.2.2. Adaptive and eﬃcient sequence matching against robot
motion variations
The original SeqSLAM assumes a constant speed during theacquisition of training and input image sequences; the matrix
is searched for the best line which minimizes the total diﬀerence. This assumption is sometimes violated in the case of mobile robots because they need to adjust their speed adaptively tothe surrounding situation for, for example, avoiding a collision
and/or not scaring people by getting too close. We therefore
use a DP to cope with such speed variations during image acquisition. We also eﬀectively utilize the history of movement toincrease reliability and reduce the calculation cost.
DP Matching: DP (Dynamic Programming) matching [12]is a tool for calculating a match between two data sequences at
non-constant intervals between data. Fig. 4 shows an exampleof DP matching for image sequences with non-constant robot
motions; a linear matching is not suitable for this case. We set alimitation on the speed diﬀerence between the training and thenavigation phase and apply the DP matching for obtaining thebest matched image pairs with an evaluation. In addition, unlike
SeqSLAM, we use the latest frame as a representative image ofa sequence so that the current location is estimated on-line.

Time, tTime, tT - ds

4.1. Local mappingT

Template numberT

Template number

T - ds220225(a) line-based matching is not optimal.

(b) DP matching can find the best matches.

Figure 4: DP matching between training and navigational image sequences. In
the matrix, darker elements have smaller diﬀerences.
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Markov localization: Mobile robot localization often uses
a movement history, which is eﬀective to limit the possible
robot positions in prediction. Miura and Yamamoto [7] adopted
a Markov localization strategy in a view-based localization. In235[7], a discrete set of locations are provided for localization anda probabilistic model of transitions between locations was usedin the prediction step. This can reduce not only localization
failures but also the calculation cost with a limited number ofsequence matches.
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The Markov localization here is formulated as follows:∑ˆBel(l)←
Pm (l|l′ )Bel(l′ ),(5)l′Bel(l) ←Po (s|l)

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=

ˆ
αPo (s|l) Bel(l),
S min
,
Sl

(6)
(7)

245

where Pm (l|l′ ) denotes the transition probability from frame l′
to l, Bel(l) the belief of the robot being location l, Po (s|l) the
likelihood of location l with sensing s, which is calculated by250
the minimum matching score of the DP matching divided by
the score for location l.
The state transition model Pm (l|l′ ) is determined by considering the image acquisition interval and the robot’s motion patterns. Currently, the training images are taken at about one me255
ter interval and the robot takes one image per two seconds withmoving at 1 m/s. Since the robot speed changes frequently due
to various reasons such as collision avoidance and turning motions, we use a transition model in which the robot may moveto locations corresponding to one of the current and the three260
subsequent location with equal probabilities.
4. Local Path Planning using View-Based Localization Results

A 3D LiDAR is used to create local 2D maps, which is forfinding free spaces. We detect obstacles in two ways. One is
to use a height map which detects regions with relatively highobstacles. We use a grid coordinate at 10 cm intervals for representing height maps. The pose of the range sensors relative
to the ground plane is estimated by fitting a plane to the data
points in the region in front of the robot.
The other way is to find low steps. Since it is sometimes
diﬃcult to find such steps only from the height due to a limited
ranging accuracy and the error in ground plane estimation, we
examine the diﬀerentiation of height data. A region with a largeheight diﬀerence with a certain number of data points is considered to be an obstacle region (i.e., A low step). Fig. 5(b) shows
an example of obstacle detection in a real scene as shown in
Fig. 5(a). The obstacles including low steps in the surrounding
environment are successfully detected.
The point cloud of the detected obstacle is periodically fed
to GMapping[16] that is a well-known SLAM utilizing the RaoBlackwellized particle filter[17] to generate a local map at eachposition (Fig. 5(c)).
4.2. Subgoal selection and path planning
The direction to move is suggested by the proposed SeqSLAM++, however, it is not always possible to move in that direction due to obstacles. Since the free spaces are recognized
only in the local map, we need to set a subgoal in the local map
which is safe and leads the robot to the destination. We here
adopt the concept of frontier which is often used in exploration
planning in an unknown space [13]. A frontier point is a point
which is free and adjacent to an unknown point. Such a point iseither inside the local map or on the edge of the map. All frontier points in front of the robot are detected as candidates for thesubgoal by examining each free cell in the local map whether it
has any unknown points in the 8-neighbor cells around it. The
most appropriate frontier point is then selected as the subgoal
which has the minimum orientational diﬀerence with the suggested moving direction. Fig. 5(d) shows an example selectionof subgoal (frontier).
Once the subgoal is set, the path toward it is generated. We
use the A* and the Dynamic Window Approach (DWA) algorithm for global and local path planning, respectively[14, 15].
The SeqSLAM++ executes the view-based global localization
at a certain interval, and a new subgoal is generated one after
another according to the localization and moving direction estimation results.
5. Experiments

5.1. Experimental setup
The proposed SeqSLAM++ localizes the robot position and
Fig. 6 shows the mobile robot used in this research. It
provides the direction to move. That information by itself is,however, not enough for guiding an actual robot safely. We265 is based on an electric wheelchair (Patrafour by Toyota Motor
East Japan Inc.) equipped with a wide-angle camera (Omni60
therefore have implemented a local navigation system which
by Occam Vision Group) for view-based localizations, and 3D
includes local mapping, subgoal selection, and path planning.
LiDAR(s) (Two FX-8x by Nippon Signal Co. or HDL-32e
by Velodyne LiDAR, Inc.) for local mapping and finding free4

(a) Scene.

(b) Obstacle detection.

(c) Local mapping and obstacle inflation. (d) Frontier detection and path planning.

Figure 5: Obstacle detection and subgoal selection : In a scene (a), the mobile robot detects obstacles based on a height map and projects them onto a 2D gridmap (b). Feeding the obstacles into GMapping periodically, the robot builds a local 2D map at each position and finds the collision-free space (c) by inflating it. A
subgoal is determined by selecting the frontier point that has the minimum orientation diﬀerence with the moving direction suggested by SeqSLAM++, then thepath toward it (d) is generated with A* and DWA.

HDL-32e
Velodyne LiDAR

Omni 60
Occam Vision Groupcb

Route A
d

a
f
j
i

h

g

Route B

Figure 7: Experiment environment and routes for the navigation performance
evaluation.

Patrafour
Toyota Motor East Japan Inc.

Table 1: Summaries of the training and testing image sets.

Figure 6: Our robot equipped with a wide-angle camera and a 3D LiDAR.

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spaces to generate subgoals. The HDL-32e has 360◦ horizontal
FOV and measures the structure of the surrounding environment. Although the Omni60 also has 360◦ horizontal FOV, thefrontal 90◦ and180◦ parts are used for capturing the training andinput images, respectively.
The following experiments were carried out in the campus295
of Toyohashi University of Technology. As shown in Fig. 7,
two routes are defined: Route A is about 300 m long and relatively straight while Route B has some curves where the mobile
robot needs to make a sharp turn to follow it.
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e

5.2. Oﬀ-line localization
In this experiment, we manually moved the robot making it
follow route A and acquired a set of training images and two
sets of test images. The training and the first test image sets305
were taken while the robot moved along the route. On the other
hand, the second test image set was captured as the robot moved
in a zig-zag so that the direction of the robot changes largely
from position to position. The image sets are shown in Fig. 8and the detail is summarized in Table 1.
310
We compared the localization performance of the SeqSLAM and the proposed SeqSLAM++ for the test image sequences. Fig. 9 shows the localization results. Note that since
the original SeqSLAM exhibited quite a low performance for5

Training
Test 1
Test 2

Camera90◦ FOV180◦ FOV180◦ FOV

Date and weather
March 5, 2015, Fine
July 11, 2015, Cloudy
July 11, 2015, Cloudy# of images
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263
282

Robot motion
SmoothSmoothZig-zagthe second test image sequence due to a large variation of therobot’s heading, we additionally implemented the same horizontal scanning in the SeqSLAM to find the best matched position in the wide image. Both comparison results show that the
proposed method exhibits a much better performance.
Fig. 10 shows the evaluation of localization accuracy. The
ground truth is determined by manually comparing the training
and test images. When an input image is judged to be locatedbetween two consecutive training images, the true position is
set in the middle of the training images. The maximum frame
diﬀerence by the proposed method is two for most of frames,
meaning the maximum localization error is about 2 m because
the training images are acquired at about one meter interval.
Table 2 summarizes the performance in terms of localization
success rate and direction selection success rate. Localizationis considered success when the diﬀerence is within two frames,
while the direction is considered correctly selected when the
directional diﬀerence is less than 5◦ . Fig. 11 shows a scene
where the proposed and the SeqSLAM with horizontal scanningsuggest diﬀerent moving directions. Since SeqSLAM does adirect comparison of (normalized) pixel values, it is sometimes

(a) Training image sequence along the route A.

(b) Input image sequence with smooth motion (test1).

(c) Input image sequence with zig-zag motion (test2).

Figure 8: Image sets captured along the route A in Fig. 7 for oﬀ-line localization experiment.

Table 2: Quantitative evaluation results.
Test image
Test 1
Test 2(a) Test image sequence 1 (Smooth).

MethodSeqSLAMSeqSLAM++SeqSLAMSeqSLAM++

Localization success rate (%)
86.1
99.6
74.2
95.8

Direction selection success rate (%)
99.6
63.1
95.8

(b) Test image sequence 2 (Zig-zag).
SeqSLAM+SCAN

Figure 9: Localization results.

training image

Proposed

Figure 11: Comparison in selecting the moving direction.
(a) Test image sequence 1 (Smooth).

(b) Test image sequence 2 (Zig-zag).

Figure 10: Localization accuracy.

weak to scenes with less textures as shown in the figure.
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5.3. Autonomous navigation
Next, we conducted autonomous navigation experiments in
the routes shown in Fig. 7. In the same way as in Sec. 5.2,
training datasets were captured manually by moving the robot
to follow the routes. On the other hand, the input images were335
taken in real time while the mobile robot was controlling itself
according to the outputs of SeqSLAM++. Fig. 12 shows snapshots of the runs, and the names of the locations correspond tosymbols in Fig. 7.
As shown in Fig. 12, the robot successfully completed the340
autonomous navigation in each routes localizing its position

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based on the view sequence. Additionally, some parts of theactual trajectories during the navigation were measured to evaluate the accuracy of the robot motion with the LiDAR-based
localization technique that was capable of estimating the robot
position correctly by comparing the global 3D map created inadvance and the local range data with Normal Distribution Transformation (NDT) [35].
As shown in Fig. 13, the mobile robot follows the training
routes properly and the proposed SeqSLAM++ combined withlocal planning works on-line with high reliability. When it wasdiﬃcult to exactly follow the path and when the robot slightly
went oﬀ the target route due to static/dynamic obstacles or the
structure of the surrounding environment, local path planningprovided appropriate paths to keep the robot traveling in theright direction given by SeqSLAM++.

Location a.

Location f.

Location b.

Location g.

Location c.

Location h.

Location d.

Location i.

Location e.

Location j.

(a) Route A.

(b) Route B.

Figure 12: Navigation experiments. Left:Robot in motion. Center:Estimated location (i.e., best matched training image). Right:Input image and selected moving
direction.

6. Conclusions and Future Work

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that is, more variations in weather, season, surrounding objects(buildings or forests), and so on.
At the same time, toward an inexpensive and easily applicable navigation system, it is essential to develop an alternative
local mapping algorithm with visual SLAM techniques. In thecurrent system, geometric information around the mobile robot
is obtained with an expensive 3D LiDAR. Instead of using thatkind of range sensor, we will build a comprehensive system that
performs the global-level localization with SeqSLAM++ utilizing local-level motion planning with 3D reconstruction techniques for a mobile robot equipped only with digital cameras.

In this paper, we proposed a new view-based localizationand navigation system SeqSLAM++ by extending the original
SeqSLAM to deal with several issues in mobile robot navigation. The SeqSLAM++ is capable of localizing the robot ro-370
bustly even when the input images have deformations or view
direction changes from the training images due to the robot motion by taking advantage of HOG feature and DP dissimilarity
calculation in the sequence matching, the horizontal scanningin the wider input image, and the probabilistic model based on375
Markov localization strategy. The reliability has been shown in
the oﬀ-line evaluations in an outdoor environment, and achieved
Acknowledgment
95.8% localization success rate against the dataset with large
This work is in part supported by JSPS KAKENHI Grantview direction changes.
Number 25280093.
In addition, we have also developed an autonomous systemthat can navigate itself given an image sequence of the target
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