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122 Xue et al. / Front Inform Technol Electron Eng 2017 18(1):122-138

Frontiers of Information Technology & Electronic Engineering

www.zju.edu.cn/jzus; engineering.cae.cn; www.springerlink.com

ISSN 2095-9184 (print); ISSN 2095-9230 (online)

E-mail: jzus@zju.edu.cn

A vision-centered multi-sensor fusing approach to

self-localization and obstacle perception for robotic cars

∗

Jian-ru XUE

†

, Di WANG, Shao-yi DU, Di-xiao CUI, Yong HUANG, Nan-ning ZHENG

(Lab of Visual Cognitive Computing and Intelligent Vehicle, Xi’an Jiaotong University, Xi’an 710049, China)

†

E-mail: jrxue@xjtu.edu.cn

Received Dec. 29, 2016; Revision accepted Jan. 8, 2017; Crosschecked Jan. 10, 2017

Abstract: Most state-of-the-art robotic cars’ perception systems are quite diﬀerent from the way a human driver

understands traﬃc environments. First, humans assimilate information from the traﬃc scene mainly through visual

perception, while the machine perception of traﬃc environments needs to fuse information from several diﬀerent kinds

of sensors to meet safety-critical requirements. Second, a robotic car requires nearly 100% correct perception results

for its autonomous driving, while an experienced human driver works well with dynamic traﬃc environments, in

which machine perception could easily produce noisy perception results. In this paper, we propose a vision-centered

multi-sensor fusing framework for a traﬃc environment perception approach to autonomous driving, which fuses

camera, LIDAR, and GIS information consistently via both geometrical and semantic constraints for eﬃcient self-

localization and obstacle perception. We also discuss robust machine vision algorithms that have been successfully

integrated with the framework and address multiple levels of machine vision techniques, from collecting training

data, eﬃciently processing sensor data, and extracting low-level features, to higher-level object and environment

mapping. The proposed framework has been tested extensively in actual urban scenes with our self-developed robotic

cars for eight years. The empirical results validate its robustness and eﬃciency.

Key words: Visual perception; Self-localization; Mapping; Motion planning; Robotic car

http://dx.doi.org/10.1631/FITEE.1601873 CLC number: TP181

1 Introduction

Rapid integration of artiﬁcial intelligence with

applications has provided some notable break-

throughs in recent years (Pan, 2016). The robotic car

is one such disruptive technology that may enter real

life in the near future, and this is also a good example

of a hybrid artiﬁcial intelligence system (Zheng et al.,

2017). A robotic car needs to answer three questions

during all the time of its driving: where it is, where

it is going, and how to go. To answer these ques-

tions, the robotic car needs to integrate three cou-

pled consequential tasks: self-localization, decision

Project supported by the National Key Program Project of

China (No. 2016YFB1001004) and the National Natural Science

Foundation of China (Nos. 91320301 and 61273252)

ORCID: Jian-ru XUE, http://orcid.org/0000-0002-4994-9343



Zhejiang University and Springer-Verlag Berlin Heidelberg 2017

making and motion planning, and motion control.

Among these tasks, the ability to understand the

robot’s surroundings lies at the core, and the robotic

car’s performance heavily depends on the accuracy

and reliability of its environment perception tech-

nologies including self-localization and perception of

obstacles.

Almost all relevant information required for au-

tonomous driving can be acquired through vision

sensors. This includes but goes well beyond lane

geometry, drivable road segments, traﬃc signs, traf-

ﬁc lights, obstacle positions and velocity, and obsta-

cle class. However, exploiting this potential of vision

sensors imposes more diﬃculties than LIDAR, radar,

or ultrasonic sensors. The sensing data of LIDAR,

radar, or ultrasonic sensors involve distance and/or

Xue et al. / Front Inform Technol Electron Eng 2017 18(1):122-138 123

velocity, i.e., information necessary for vehicle con-

trol. Nevertheless, camera-based driver assistance

systems have entered the automotive markets (Ul-

rich, 2016). However, the computer vision based ap-

proach for autonomous driving in urban environment

is still an open research issue, since these state-of-the-

art vision technologies are still incapable of providing

the high rate of success demanded by autonomous

driving. Fortunately, recent approaches to scene un-

derstanding using deep learning technologies suggest

a promising future of vision-centered approach for

robotic cars (Hoiem et al., 2015).

In this paper, we propose a vision-centered

multi-sensor fusing framework for the robotic cars’

perception problem, which fuses camera, LIDAR,

and GIS information consistently via geometrical

constraints and driving knowledge. The framework

consists of self-localization and processing of obsta-

cles surrounding the robotic car. At ﬁrst glance these

two problems seem to have been well studied, and

early works in this ﬁeld were quickly rewarded with

promising results. However, the large variety of sce-

narios and the high rates of success demanded by

autonomous driving have kept this research alive.

Speciﬁcally, integrating computer vision algorithms

within a compact and consistent machine perception

system is still a challenging problem in the ﬁeld of

robotic cars.

Self-localization is the ﬁrst critical problem of

the aforementioned challenges. The capability of a

robot to accurately and eﬃciently determine its po-

sition at all times is one of the fundamental tasks es-

sential for a robotic car to interact with the environ-

ment. Diﬀerent accuracies and update frequencies of

self-localization are required by various applications

of a robotic car. Taking parking as an example, the

accuracy needed is at the centimeter level, and the

update frequency is about 100 Hz. In contrast, for

routing and guidance, the required accuracy is re-

duced to 10–100 m level and the update frequency

is about 0.01 Hz. To address GPS measurements’

critical problems of low accuracy and being easily

aﬀected, the map-based method becomes one of the

most popular methods for robotic cars, in which one

map is used to improve upon GPS measurements and

to ﬁll in when signals are unavailable or degraded.

In the line of the map-based localization method, an

ideal map should provide not only a geometrical rep-

resentation of the traﬃc environment, but also some

kinds of sensor-based descriptions of the environment

to alleviate the diﬃculty of self-localization as well

as of motion planning (Fuentes-Pacheco et al., 2015).

However, traditional road maps for a human driver

cannot be used directly for a robotic car, since it is

composed of evenly sampled spatial points connected

via polylines, with low accuracy, especially in urban

areas, over about 5–20 m. Inevitably, building a high

deﬁnition map becomes one of the core competencies

of robotic cars.

Mapping approaches build geometric represen-

tations of environments. They adopt sensor-based

environment description models, which integrate vi-

sual and geometric features and have been designed

in conjunction with Bayesian ﬁlters so that the

sensor-based description can be updated over time

(Douillard et al., 2009). Mapping for robotic cars

through local perception information is a challenging

problem for a number of reasons. Firstly, maps are

deﬁned over a continuous space; the solution space of

map estimation has inﬁnitely many dimensions. Sec-

ondly, learning a map is a ‘chicken-and-egg’ problem,

for which reason it is often referred to as the simulta-

neous localization and mapping (SLAM) or concur-

rent mapping and localization problem (Thrun and

Leonard, 2008). More speciﬁcally, the diﬃculty of

the mapping problem can be increased by a collection

of factors including map size, noise in perception and

actuation, perceptual ambiguity, and alignment of

spatial-temporal sensing data acquisition from diﬀer-

ent types of sensors running asynchronously. With a

given map of the traﬃc environment, self-localization

becomes the problem of determining its pose in the

map.

Another critical problem is the need for high

reliability in processing obstacles surrounding the

robotic car. This guarantees the robotic car’s safety

in driving through real traﬃc. The robotic car needs

to know positions, sizes, and velocities of the sur-

rounding obstacles to make high-level driving de-

cisions. However, real-time detection and tracking

algorithms relying on a single sensor often suﬀer

from low accuracy and poor robustness when con-

fronted with diﬃcult, real-world data (Xue et al.

2008). For example, most state-of-the-art object

trackers present noisy estimates of velocities of obsta-

cles, and are diﬃcult to track due to heavy occlusion

and viewpoint changes in the real traﬃc environment

(Ess et al., 2010; Mertz et al., 2013). Additionally,

124 Xue et al. / Front Inform Technol Electron Eng 2017 18(1):122-138

without robust estimates of velocities of nearby ob-

stacles, merging onto or oﬀ highways or changing

lanes becomes a formidable task. Similar issues

will be encountered by any robot that must act au-

tonomously in crowded, dynamic environments.

Fusing multiple LIDARs and radars is an essen-

tial module of a robotic car and of advanced driver

assistance systems. With the improvement of vision-

based object detection and tracking technologies, in-

tegrating vision technologies with LIDAR and radars

makes it possible to make a higher level of driving

decision than previous methods which fuse only LI-

DARs with radars.

In this paper, we summarize our 8-year eﬀort

on a vision-centered multi-sensor fusing approach

to the aforementioned problems, as well as lessons

we have learned through the long-term and exten-

sive test of the proposed approach with our robotic

car autonomously driving in real urban traﬃc (Xue

et al., 2008; Du et al., 2010; Cui et al., 2014; 2016).

Fig. 1 illustrates the timeline of the robotic cars we

developed for the test of the vision-centered multi-

sensor fusing approach.

2013-201520112009

201620122010

Fig. 1 The timeline of the robotic cars for the long-

term test of the vision-centered multi-sensor fusing

approach

2 Related works

In this section, we present a brief survey of re-

cent works on self-localization, and obstacle detec-

tion and tracking.

2.1 Self-localization

The core problem of self-localization is mapping,

and mapping and localization were initially stud-

ied independently. More speciﬁcally, mapping for

robotic cars is realized as a procedure of integrat-

ing local, partial, and sequential measurements of

the car’s surroundings into a consistent representa-

tion, which forms the basis for further navigation.

The key to the integration lies in the joint alignment

of spatial-temporal sensing data from multiple het-

erogenous sensors equipping the robotic car, which

is usually performed oﬀ-line.

With a given map, one needs to establish corre-

spondence between the map and its local percep-

tion, and then determines the transformation be-

tween the map coordinate system and the local per-

ception coordinate system based on these correspon-

dences. Knowing this transformation enables the

robotic car to locate the surrounding obstacles of in-

terest within its own coordinate frame—a necessary

prerequisite for the robotic car to navigate through

the obstacles. This means that the localization is ac-

tually a registration problem (Du et al., 2010), and

can be solved via map-matching technologies (Cui

et al., 2014). With its localization in the global map,

the robot can obtain navigation information from

the map. Additionally, the navigation information

can be further used as a prior in verifying the local

perception results, for the purpose of increasing the

accuracy and reliability of the local perception (Cui

et al., 2014; 2016).

Mapping and localization were eventually

known as SLAM (Dissanayake et al., 2001). SLAM

methods are able to reduce the accumulative drift

relative to the initial position of the robotic car by

using landmarks and jointly optimizing over all or

a selection of poses and landmarks. Eﬃcient opti-

mization strategies using incremental sparse matrix

factorization (Montemerlo et al., 2002) or relative

structure representation (Grisetti et al., 2010) have

been proposed to make these algorithms tractable

and scalable. Thus, at a theoretical and concep-

tual level, SLAM is now considered a solved prob-

lem in the case that LIDARs are used to build 2D

maps of small static indoor environments (Thrun and

Leonard, 2008). Comprehensive surveys and tuto-

rial papers on SLAM can be found in the literature

(Durrant-Whyte and Bailey, 2006).

For large-scale localization and mapping,

metric-topological mapping constructs maps that

navigate between places which can be recognized

perceptually (Blanco et al., 2007). A popular rep-

resentation is to use sub-maps that are metrically

consistent, and connect them with topological con-

straints. Generating such a metric-topological map

is based on the reconstruction of the robot path in a

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