视觉里程计参考论文及数据集下载百度网盘地址

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PL-SLAM: a Stereo SLAM System through the Combination

of Points and Line Segments

Ruben Gomez-Ojeda, David Zuñiga-Noël, Francisco-Angel Moreno,

Davide Scaramuzza, and Javier Gonzalez-Jimenez

Abstract—Traditional approaches to stereo visual

SLAM rely on point features to estimate the camera

trajectory and build a map of the environment. In low-

textured environments, though, it is often diﬃcult to

ﬁnd a suﬃcient number of reliable point features and,

as a consequence, the performance of such algorithms

degrades. This paper proposes PL-SLAM, a stereo

visual SLAM system that combines both points and

line segments to work robustly in a wider variety of

scenarios, particularly in those where point features

are scarce or not well-distributed in the image. PL-

SLAM leverages both points and segments at all the

instances of the process: visual odometry, keyframe

selection, bundle adjustment, etc. We contribute also

with a loop closure procedure through a novel bag-of-

words approach that exploits the combined descriptive

power of the two kinds of features. Additionally, the

resulting map is richer and more diverse in 3D ele-

ments, which can be exploited to infer valuable, high-

level scene structures like planes, empty spaces, ground

plane, etc. (not addressed in this work). Our proposal

has been tested with several popular datasets (such as

KITTI and EuRoC), and is compared to state of the

art methods like ORB-SLAM, revealing a more robust

performance in most of the experiments, while still

running in real-time. An open source version of the

PL-SLAM C++ code will be released for the beneﬁt

of the community.

Index Terms—Stereo Visual SLAM, line segment

features, bundle adjustment, loop closure

I. Introduction

In recent years, visual Simultaneous Localization And

Mapping (SLAM) is ﬁrmly progressing towards the degree

of reliability required for fully autonomous vehicles: mobile

robots, self-driving cars or Unmanned Aerial Vehicles

(UAVs). In a nutshell, the SLAM problem consists of the

estimation of the vehicle trajectory given as a set of poses

(position and orientation), while simultaneously building

a map of the environment. Apart from self-localization, a

map becomes useful for obstacle avoidance, object recog-

nition, task planning, etc. [1].

As a ﬁrst-level classiﬁcation, SLAM systems can be di-

vided into topological (e.g. [2]–[5]) and metric approaches.

This work has been supported by the Spanish Government (project

DPI2017-84827-R and grant BES-2015-071606) and the Andalusian

Government (project TEP2012-530).

R. Gomez-Ojeda, F.A. Moreno, D. Zuñiga-Noël, and J. Gonzalez-

Jimenez are with the Machine Perception and Intelligent

Robotics (MAPIR) Group, University of Malaga. (email:

rubengooj@gmail.com).

D. Scaramuzza is with the Robotics and Perception Group, Dep.

of Informatics, University of Zurich, and Dep. of Neuroinformatics,

University of Zurich and ETH Zurich, Switzerland.

(a) lt-easy (b) euroc/V2-01-easy

Figure 1. Low-textured environments are challenging for feature-

based SLAM systems based on traditional keypoints. In contrast,

line segments are usually common in human-made environments,

and apart from an improved camera localization, the built maps are

richer as they are populated with more meaningful elements (3D line-

segments).

In this paper, we focus on the latter, which take into

account the geometric information of the environment and

build a physically meaningful map of it [6], [7]. These

approaches can be further classiﬁed into direct and feature-

based systems. The ﬁrst group, i.e. direct methods, esti-

mates the camera motion by minimizing the photometric

errors between consecutive frames under the assumption of

constant brightness along the local parts of the sequences

(examples of this approach can be found elsewhere [8]–

[10]). While this group of techniques has the advantage

of working directly with the input images regardless of

any intermediate representation, they are very sensitive to

brightness changes (this phenomena was addressed in [11])

and constrained to narrow baseline motions. In contrast,

feature-based methods employ an indirect representation

of the images, typically in the form of point features, that

are tracked along the successive frames and then employed

for recovering the pose by minimizing the projection errors

[12], [13].

It is noticeable that the performance of any of the above-

mentioned approaches usually decreases in low-textured

environments in which it is typically diﬃcult to ﬁnd a

large set of keypoint features. The eﬀect in such cases is

an accuracy impoverishment and, occasionally, the com-

plete failure of the system. Many of such low-textured

arXiv:1705.09479v2 [cs.CV] 9 Apr 2018

environments, however, contain planar elements that are

rich in linear shapes, so it would be possible to extract

line segments from them. We claim that these two types of

features (keypoints and segments) complement each other

and its combination leads to a more versatile, robust and

stable SLAM system. Furthermore, the resulting maps

comprising both 3D points and segments provide more

structural information from the environment than point-

only maps, as can be seen in the example shown in Figure

1(d). Thus, applications that perform high-level tasks such

as place recognition, semantic mapping or task planning,

among others, can signiﬁcantly beneﬁt from the richer

information that can be inferred from them.

These beneﬁts, though, come at the expense of a higher

computational burden in both detecting and matching

line-segments in images [14], and also in dealing eﬀectively

with segment-speciﬁc problems like partial occlusions, line

disconnection, etc. which complicate feature tracking and

matching as well as the residual computation for the map

and pose optimization. Such hurdles are the reason why

the number of solutions that have been proposed in the

literature to SLAM or Structure from Motion (SfM) with

line features (e.g. [15]–[19]) is so limited. Besides, the

few solutions we have found only perform robustly in

highly structured environments while showing unreliable

results when applied to more realistic ones such as those

recorded in the KITTI or EuRoC datasets. In this work,

we address the segment-speciﬁc tracking and matching

issues by discarding outliers through the comparison of

the length and the orientation of the line features, while,

for the residual computation, we represent segments in the

map with their endpoints coordinates. Thus, the residuals

between the observed segments and their corresponding

lines in the map are computed by the distance between the

projections of those endpoints on the image plane and the

inﬁnite lines associated to the observed ones. This way, we

are able to build a consistent cost function that seamlessly

encompasses both point and line features.

These two kinds of features are also employed to ro-

bustly detect loop closures during robot navigation, fol-

lowing a new bag-of-words approach that combines the

advantages of using each of them to perform place recog-

nition. In summary, we propose a novel and versatile stereo

visual SLAM system, coined PL-SLAM, which builds upon

our previous Visual Odometry approach presented in [20],

and combines both point and line segment features to

perform real-time robot localization and mapping. The

main contributions of this work are:

◦ The ﬁrst open source stereo SLAM system that employs

point and line segment features in real time, hence

being capable of operating robustly in low-textured

environments where traditional point-only approaches

tend to fail, while obtaining similar accuracy in the

rest of the scenarios. Because of the consideration of

both kinds of features, our proposal also produces rich

geometrical maps.

◦ A new implementation of the bundle adjustment process

that seamlessly accounts for both kinds of features while

reﬁning the poses of the keyframes.

◦ An extension of the bag-of-words approach presented

in [21] that takes into account the description of both

points and line segments to improve the loop-closure

process.

A set of illustrative videos showing the performance

of proposed system and an open source version

of the developed C++ PL-SLAM library are

publicly available at http://mapir.uma.es and

https://github.com/rubengooj/pl-slam.

II. Related Work

Feature-based SLAM is traditionally addressed by

tracking keypoints along successive frames and then

minimizing some error function (typically based on re-

projection errors) to estimate the robot poses [22]. Among

the most successful proposals we can highlight FastSLAM

[23], PTAM [24] [25], SVO [26] [10], and, more recently,

ORB-SLAM [13], which relies on a fast and continuous

tracking of ORB features [27], and a local bundle adjust-

ment step with the continuous observations of the point

features. However, all of the previous approaches tend

to fail or reduce their accuracy in low-textured scenarios

where the lack of repeatable and reliable features usually

hinders the feature tracking process. In the following, we

review the state of the art of SLAM systems based on

alternative image features to keypoints: i.e. edgelets, lines,

or line segments.

One of the remarkable approaches that employs line

features is the one in [28], where the authors propose an

algorithm to integrate them into a monocular Extended

Kalman Filter SLAM system (EKF-SLAM). In the refered

paper, the line detection relies on an hypothesize-and-test

method that connects several near keypoints to achieve

real-time performance. Other works employ edge land-

marks as features in monocular SLAM, as the one reported

in [29], which does not only include the information of

the local planar patch as in the case of keypoints, but

also considers local edge segments, hence introducing new

valuable information as the orientation of the so-called

edgelets. In that work they derive suitable models for those

kinds of features and use them within a particle-ﬁlter

SLAM system, achieving nearly real-time performance.

More recently, authors in [10] also introduced edgelets in

combination with intensity corners in order to improve

robustness in environments with little or high-frequency

texture.

A diﬀerent approach, known as model-based, incor-

porates prior information about the orientation of the

landmarks derived from line segments. Particularly, the

method in [30] presents a monocular 2D SLAM system

that employs vertical and horizontal lines on the ﬂoor as

features for both motion and map estimation. For that,

they propose two diﬀerent parameterizations for the verti-

cal and the horizontal lines: vertical lines are represented

as 2D points on the ﬂoor plane (placed the intersection

point between the line and such plane), while horizontal

lines are represented by their two end-points placed on

the ﬂoor. Finally, the proposed models is incorporated into

an EKF-SLAM system. Another model-based approach is

reported in [31], where the authors introduce structural

lines in an extension of a standard EKF-SLAM system.

The dominant directions of the lines are estimated by com-

puting their vanishing points under the assumption of a

Manhattan world [32]. All these model-based approaches,

though, are limited to very structured scenarios and/or

planar motions, as they rely solely on line features.

The works in [16], [33] address a generic approach

that compares the impact of eight diﬀerent landmark

parametrization for monocular EKF-SLAM, including the

use of point and line features. Nevertheless, such systems

are only validated through analytic and statistical tools

that assumed already known data association and that,

unlike our proposal, do not implement a complete front-

end that detect and track the line segments. Another

technique for building a 3D line-based SLAM system has

been proposed in the recent work [34]. For that, the

authors employ two diﬀerent representations for the line

segments: the Plücker line coordinates for the initialization

and 3D projections, and an orthonormal representation

for the back-end optimization. Unfortunately, neither the

source code is available nor the employed dataset contain

any ground-truth, therefore it has not been possible to

carry out a comparison against our proposal.

Recently, line segment features have also been employed

for monocular pose estimation in combination with points,

due to the bad-conditioned nature of this problem. For

that, in [35] the authors extended the semi-direct approach

in [26] with line segments. Thanks to this pipeline, line

segments can be propagated eﬃciently throughout the

image sequence, while reﬁning the position of the end-

points under the assumptions of high frame rate and very

narrow-baseline.

Finally, by the time of the ﬁrst submission of this

paper, a work with the same name (PL-SLAM, [36]) was

published extending the monocular algorithm ORB-SLAM

to the case of including line segment features computed

through the LSD detector [37]. Apart from being a monoc-

ular system (unlike our stereo approach), their proposal

deals with line tracking and matching in an essentially

diﬀerent way: they propagate the line segments by their

endpoints and then perform descriptor-based tracking,

which increases the computational burden of ORB-SLAM.

Besides this computational drawback, when working with

features detected with the LSD detector, the variance

of the endpoints becomes quite pronounced, specially in

challenging illumination conditions or very low-textured

scenes, making more diﬃcult wide-baseline tracking and

matching between line features in non-consecutive frames.

Our PL-SLAM approach, in contrast, does not make any

assumption regarding the position of the lines endpoints

so that our tracking front-end allows to handle partially

occluded line segments, endpoints variance, etc., for both

the stereo and frame-to-frame tracking, hence becoming a

more robust approach to point-and-line SLAM.

New Frame

is it a KF?

Feature

Extraction

Stereo

Matching

Frame-to-frame

Tracking

Motion

Estimation

Stereo Visual Odometry

Search new

matches with

other KFs in

the map

Local Mapping

Get visual

descriptor

Query

bag-of-words

Compute

pose change

Loop

correction

Loop Closing

Insert

new KF

Perform

local BA

Map

Keyframes

Covisibility

graph

Spanning tree

Graph

3D Line

segments

3D points

Landmarks

yes

Figure 2. Scheme of the stereo PL-SLAM system.

III. PL-SLAM Overview

The general structure of the PL-SLAM system proposed

here is depicted in Figure 2, and its main modules are

described in the following sections. As it is common to

other SLAM systems (being ORB-SLAM [13] the most

popular method nowadays), our proposal is also based

on three diﬀerent threads: visual odometry, local mapping,

and loop closure. This eﬃcient distribution allows for a

continuous tracking of the VO module while the local

mapping and the loop closure ones are processed in the

background only when a new keyframe is inserted.

Map. The map consists of i) a set of keyframes (KFs),

ii) the detected 3D landmarks (both keypoints and line

segments), iii) a covisibility graph and iv) a spanning tree.

The keyframes contain the observed stereo features and

their descriptors, a visual descriptor of the corresponding

left image computed through a visual vocabulary as ex-

plained later in Section VI-A, and the information of the

3D camera pose.

Regarding the landmarks, we store the list of obser-

vations and the most representative descriptor for each

detected landmark. Besides, speciﬁcally for points, we

also keep its estimated 3D position while, for the line

segments, we keep both their direction and the estimated

3D coordinates of their endpoints.

Finally, the covisibility information, as in [38], is mod-

eled by a graph: each node represents a KF, and edges

between KFs are created only if they share a minimum

number of landmarks, which in this work is set to 20

landmarks (see Figure 3 for an example), allowing for real-

time bundle adjustment along the local map.

Similarly, in order to perform a faster loop closure

optimization, we also form the so-called essential graph,

which is less dense than the covisibility graph because an

edge between two KFs is created when they share more

than 100 landmark observations. Finally, the map also

contains a spanning tree, which is the minimum connected

representation of a graph that includes all the KFs.

Feature Tracking. We perform feature tracking

through the stereo visual odometry algorithm from our

previous work [20]. In a nutshell, we track image fea-

tures (points and segments) from a sequence of stereo

frames and compute their 3D position and their associated

uncertainty represented by covariance matrices. The 3D

landmarks are then projected to the new camera pose, and

the projection errors are minimized in order to obtain both

the camera pose increment and the covariance associated

to such estimation. This process is repeated every new

frame, performing simply frame to frame VO, until a new

KF is inserted to the map. Further discussion about this

feature tracking procedure will be formally addressed in

Section IV. Once a KF is inserted into the map, two

procedures are run in parallel: local mapping and loop

closure detection.

Local Mapping. The local mapping procedure looks

for new feature correspondences between the new KF, the

last one and those connected to the last one in the cov-

isibility graph. This way, we build the so-called local map

of the current KF, which includes all the KFs that share

at least 20 landmark observations with the current one as

well as all the landmarks observed by them. Finally, an

optimization of all the elements within the local map (KF

poses and landmarks positions) is performed. A detailed

description of this procedure will be presented in Section

Loop Closure. In parallel to local mapping, a loop

closure detection is carried out by extracting a visual

descriptor for each image, based on a bag-of-words ap-

proach, as will be described in Section VI. All the visual

descriptors of the captured frames during camera motion

are stored in a database, which is later employed to ﬁnd

similar frames to the current one. The best match will

be considered a loop closure candidate only if the local

sequence surrounding this KF is also similar. Finally, the

relative SE(3) transformation between the current KF and

the loop closure candidate is estimated so that, if a proper

estimation is found, all the KFs poses involved in the loop

are corrected through a pose-graph optimization (PGO)

process.

It is important to remark that the stereo visual odom-

etry system runs continuously at every frame while both

the local mapping and loop closure detection procedures

are launched in background (in separated threads) only

when a new KF is inserted, thus allowing our system

to reach real-time performance. In the event of a new

keyframe being inserted in the system while the local

mapping thread is still being processed, the keyframe is

temporary stored until the map is updated and then a

new local mapping process is launched.

These mapping and loop closure approaches are identi-

cal to the ones followed in ORB-SLAM, being aimed to

reduce the high computational burden that general BA

involves (along with the incorporation of recent sparse al-

Figure 3. Covisibility graph in the sequence lt-ﬁrst for which we have

represented the edges connecting the keyframes with green lines.

gebra techniques). Within the BA framework, our proposal

belongs to the so-called relative techniques (e.g. [39]–[41]),

which have gained great popularity in the last years as an

alternative to the more costly global approaches (e.g. [24],

[42]).

IV. Feature Tracking

This section reviews the most important aspects of our

previous work [20], which deals with the visual odometry

estimation between consecutive frames, and also with

the KF decision policy. Basically, both points and line

segments are tracked along a sequence of stereo frames

(see Figure 1), and then the 3D motion of the camera

(and also its uncertainty) is computed by minimizing the

projection errors.

A. Point Features

In this work we use the well-known ORB method [27]

due to its great performance for keypoint detection, and

the binary nature of the descriptor it provides, which

allows for a fast, eﬃcient keypoint matching. In order to

reduce the number of outliers, we only consider measure-

ments which fulﬁll that the best match in the left image

corresponds to the best match in the right one, i.e. they

are mutual best matches. Finally, we also ﬁlter out those

matches whose distance in the descriptor space with the

second best match is less than twice the distance with

the best match, to ensure that the correspondences are

meaningful enough.

B. Line Segment Features

The Line Segment Detector (LSD) method [37] has been

employed to extract line segments, providing high preci-

sion and repeatability. For stereo matching and frame-to-

frame tracking we augment line segments with a binary

descriptor provided by the Line Band Descriptor (LBD)

method [43], which allows us to ﬁnd correspondences

between lines based on their local appearance. Similarly to

the case of points, we check that both candidate features

are mutual best matches, and also that the feature is

meaningful enough. Finally, we take advantage of the

useful geometrical information that line segments provide

in order to ﬁlter out those line matches with diﬀerent

orientations and lengths, and those with a high diﬀerence

on the disparities of the endpoint. Notice that this ﬁlter

helps the system to retain a larger amount of structural

资源文件列表:

视觉里程计参考论文及数据集下载百度网盘地址.zip 大约有9个文件

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