# Indoor Navigation With the Smart Environment Explorer Stick (SEES)

Xiong Min Lin, Jean Connier, Philippe Vaslin, C.C. Guo, T.Y. Zang, Jian-Jin

Li, Christophe de Vaulx, Kun Mean Hou

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Xiong Min Lin, Jean Connier, Philippe Vaslin, C.C. Guo, T.Y. Zang, et al., Indoor Navigation With the Smart Environment Explorer Stick (SEES). New Information Communication Sciences and Technology for Sustainable Development (NICST 2015), Sep 2015, Bordeaux, France. hal-05009821

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Distributed under a Creative Commons Attribution 4.0 International License
Indoor Navigation With the Smart
Environment Explorer Stick(SEES)

X.M. Lin #¹, #¹, J. Connier #², P. Vaslin #³, C.C. Guo #², T.Y. Zang &¹, J.J. Li #⁴, C. De Vaulx #⁵, K.M. Hou #⁶

*Laboratoire d'Informatique de Modélisation et d'Optimisation des Systèmes

Université Blaise Pascal, France

¹ linxiongmin@whu.edu.cn, ² connier@poste.isima.fr, ³ vaslin@isima.fr, ⁴ jjli@isima.fr,

⁵ christophe.devaulx@cust.univ-bpclermont.fr, ⁶ kun-mean.hou@isima.fr

* Internet & IT lab, Electronic Information School

Wuhan University, China

² netccg@whu.edu.cn

& School of Computer Science

Harbin Institute of Technology, China

¹ tianyi.zang@hit.edu.cn

**Abstract**—Visually Impaired People (VIP) spend most of their time indoors because of their mobility difficulties caused by poor vision. Indoor navigation is thus a very important topic for them. The Smart Environment Explorer Stick project (SEES) has started in 2013 and aims to provide a tool to enhance the mobility of VIP. This paper focuses on the evaluation of sensors that support the core functionalities of the smart stick, i.e. navigation, environment perception and obstacle detection. These sensors are the following: a wheel encoder, a gyroscope, an accelerometer, ultrasonic sensors and a camera. Several experiments have been performed on a prototype platform, giving a better idea of how to achieve real-time robust navigation, environment perception and obstacle detection.

**Keywords:** Visually Impaired People (VIP), indoor navigation, smart stick, object detection, sensor data processing.

I. INTRODUCTION

Visually Impaired People have conditions ranging from severe reduction in vision to total blindness. According to a released report of WHO [1], 285 million people are estimated to be visually impaired worldwide, 39 million of them are blind and 246 million have low vision. The number of VIP in the world keeps increasing and most of them have to stay at home to avoid the inconvenience and risks of going out.

Numerous solutions are or have been proposed to VIP to enhance their mobility, such as guide dogs and audio interfaced GPS; however, most of them focus on outdoor navigation only. Indoor navigation would also be very useful, since VIP spend more time indoors, both in private and public spaces (e.g. malls, offices). Indeed, a previous study has shown that, on average, people spend about 90% of their time indoors [2], which means that the most important and useful landmarks are in indoor space. On the other hand, indoor spaces are much more complex than outdoor ones. Even for sighted people, it is hard to navigate and learn spatial information in large or unfamiliar indoor environments. Since VIP have more mobility difficulties than able bodied, it is important to improve their indoor mobility, and in the same time their living conditions.

Navigation assistance constraints are very different for VIP and sighted people. Firstly, the required precision is much higher with VIP: whereas able bodied people can easily find their goal at a distance of 5 meters, VIP cannot; they cannot see dangers as well. For these reasons, a navigation tool for VIP should have a positioning accuracy of about 20 centimetres. Secondly, the Human Machine Interface (HMI) is an issue on its own: as touch screens cannot be used, for instance, specific interfaces have to be developed, relying either on sound or tactile feedback. Thirdly, VIP's limited perception of their environment induces strong requirements for a navigation tool; as VIP cannot read traffic panels and maps, it is very difficult for them to notice that they are on a wrong itinerary. This makes the correctness of the localisation and direction absolutely critical. Moreover, because they cannot see environmental changes (e.g. open/closed doors, construction works), they have a hard time adapting to them. The system should be able to detect these situations and

<figure><img src="image_2.png" /><figcaption>Fig. 1: Prototype of the SEES</figcaption></figure>
provide subsequently chosen itineraries. Finally, more
"intelligence" must be embedded in systems designed for VIP.

The Smart Environment Explorer Stick project [3] has been
developed since 2013 by the SMIR team of the laboratory
LIMOS UMR 6158 CNRS (Blaise Pascal University, France).
It is aimed at being a help for VIP in their everyday life,
specifically in the domains of navigation and perception, by
providing them a real-time, low-cost and energy-efficient
smart stick. This paper focuses on the implementation and the
use of the various sensors (i.e. a wheel encoder, 3-D
accelerometers and gyroscopes, ultrasonic sensors and a
digital camera) that are currently embedded by the prototype
of SEES (Figure 1).

## II. STATE OF THE ART

Thanks to the general awareness of the population about
solutions to VIP's navigation problems, a number of assistant
tools and technologies have been developed. Some of the most
representative ones are listed below, classed in two
categories : projects focusing on obstacle avoidance and
projects focusing on indoor navigational assistance.

### A. Obstacle avoidance

The traditional white cane is the simplest and most
common tool to detect obstacles due to its low cost and its
usability. It can help VIP to detect the obstacles in front of
them. Visually impaired people can get direct sense from the
white cane and a rough feeling of the surrounding
environment. The white cane is cheap and easy to use, but its
main drawback is the shortness of the detection distance.

Another traditional method for VIP is the guide dog. With the help of a trained guide dog, VIP can go to many places. But a guide dog is unable to detect potential obstacles at head level. It takes between 3 and 9 months [4] to train a guide dog and for each guide dog, its service stage is on an average of 9 years [5] and requires regular dog up-keeping expenditure and lifestyle changes. Furthermore, it costs about 50000 pounds [6] (about 70 thousand Euro, or 500 thousand Yuan) to support a guide dog from birth to retirement.

The Teletact, developed by Farcy et al. [7] is a hand-held
laser telemeter that can be clipped on a white cane. The
distance is transmitted to the user either through a tactile or an
audio interface. This device has been commercialised and
thoroughly tested by users. But the narrow focus of the laser
beam forces the user to scan the environment to detect small
obstacles and it does not allow indoor navigation.

### B. Indoor navigation system

We have separated infrastructure-based systems from
infrastructure-free systems: the former ones require the
installation of specific devices in the environment, but are
typically simpler to implement; the latter ones do not suffer
from the same potentially prohibitive costs, but they face the
complexity needed to achieve sufficient precision and
robustness.

For VIP navigation, the necessary ubiquity of the solution
(VIP should be able to go to any places) prevents the
exclusive use of infrastructure-based methods. However, they

could be used in addition of infrastructure-free methods, if the
required support is available (e.g. Wi-Fi access points).

1) *Infrastructure-based systems:* GPS is a prime example of
infrastructure-based system, that partially solves outdoor
localisation issues. However, it does not solve the specific
case of VIP: firstly, because of its limitations for civil purpose,
GPS cannot reach sub-meter accuracy, which is not enough
for VIP [8]. Secondly, in indoor environments, GPS signal is
either blocked or degraded.

Li et al. [9] proposed a localization system that exploits the
LED lighting infrastructure to transmit information, by using
their ability to quickly switch on and off. Every lamp of the
lighting infrastructure emits a different signal, containing its
position, which is detected by a camera carried by the user.
This method is very accurate (about 0.4 m accuracy is
reported), and makes use of a necessary existing infrastructure
(lighting) instead of adding another. This infrastructure still
has non-trivial constraints: lighting must be provided by LED
bulbs that have to be paired to special circuits to generate the
broadcasted signals. Furthermore, pointing the camera to the
LEDs is a complex task for VIP.

Wi-Fi fingerprinting, described by Beder and Klepal [10], is
a commonly implemented navigation technique that relies on
surrounding Wi-Fi access points to deduce the position of a
receiver. It works by creating a radio map of the environment
and then comparing the Wi-Fi measurements of an object to
this map to get the position of this object. This method has the
advantages of constant precision with respect to time (like the
GPS) and somewhat low computational requirements. But it
has drawbacks: it needs a precise radio mapping of the
environment, and a good Wi-Fi infrastructure. Besides, the
accuracy is not high enough for VIP navigation requirements.

2) *Infrastructure-free systems:* Riehle et al. [11] propose a
system based on the magnetic signature of a building. It
avoids precise magnetic mapping of buildings by only
scanning limited routes along points of interest. However, it
still requires prior work and provides only a restricted, point-
to-point navigation solution: if the user deviates from the pre-
existing itineraries, his location will not be determined.
Suitable conditions for this system might also be restrictive as
not all buildings possess a steel structure and electrical power
systems (e.g. ancient buildings).

An indoor pedestrian navigation system using foot-
mounted IMU and ultrasound sensors has been developed by
Girard et al. [12]. This device detects the user's steps and
measures their length and orientation. Given that during the
stance phase the foot velocity is null, this information is used
to reset the integration regularly, thus limiting the error drift
that normally plagues computations involving inertial sensors.
This technique ("zero velocity update") allows to compute the
foot position at each step much more efficiently than in a
naive set-up. A pair of ultrasound range sensors is used to
measure the distance to the walls. This system combines
several advantages. Firstly it is accurate over extended time
periods, which cannot be guaranteed by methods based on
IMU sensors only. Secondly, it does not require a specific
infrastructure to be installed in buildings. There are few drawbacks to this method, mainly expressing the low robustness over map model variations: any environmental “noise”, (e. g. furniture, open or closed doors) has an effect on the precision or relevance of the ultrasound range measures; it is hence susceptible to trouble the particle filter and lead it to “take the wrong path”.

### III. MATERIAL AND METHODS

We have developed specific methods to exploit and analyse the data of the sensors embedded in the SEES prototype.

#### A. Wheel encoder

The SEES is equipped with a wheel encoder on its right wheel in order to measure the distance travelled by this wheel. A disk with 406 marks is attached to the wheel's axis, and a fixed optical sensor counts the marks passing in front of it. This count gives an estimate of the angular motion of the wheel. As the wheel radius is 18 centimetres, we can have a precision of 0.29 centimetres on the distance travelled the SEES, which is more than enough.

#### B. Accelerometer

We evaluated the 3-D accelerometer [13] in two positioning-related methods. First, we used the accelerometer to estimate the orientation of the IMU chip. Indeed, the accelerometer can sense both the driving acceleration (i.e. acceleration from motion) as well as the effect of gravity. At rest, the driving acceleration is null and only the gravity vector is measured, since this latter is collinear with the vertical axis of the terrestrial reference frame, we can derive the attitude of the chip with respect to the horizontal plane.

Then we can use the 3-D accelerometer together with the 3-D gyroscope to fully compute the SEES position using strap-down inertial navigation. This technique has the advantage of being self-contained: neither prior knowledge nor any kind of infrastructure is needed for it to function.

In strap-down navigation, the 3-D gyroscope is used to track the orientation, or attitude, of the accelerometer. The signals of the accelerometer can then be rectified to obtain the 3-D accelerations of the system along the tree axes of the chosen reference frame. A first time integration of this rectified signal yields the 3-D velocity of the system, and a second one gives its position.

#### C. 3-D Gyroscope

This 3-D gyroscope [13] can be used in two ways: in a simple way, we can integrate the angular rate on the vertical (z) axis to get the angle; in a more elaborated way, we can integrate the angular rates on the gyroscope's three axis to get the attitude in space. The first method is useful if we consider that the system is only moving in the horizontal plane, which can be assumed if we are certain that the ground is flat. However, if we want to use the accelerometer, we need a more precise estimation of the orientation that includes the three axis, in order to avoid integration mistakes with the gravity. We will describe a method to keep track of the attitude using the gyroscope. This method was presented in [14].

The orientation of the SEES reference frame, called $R'$, inside the room reference frame called $R$, can be described by a “direction cosine matrix” of the following form:

$$ \begin{pmatrix} \cos(\vec{i}, \vec{i}')\cos(\vec{i}, \vec{j}')\cos(\vec{i}, \vec{k}') \\ \cos(\vec{j}, \vec{i}')\cos(\vec{j}, \vec{j}')\cos(\vec{j}, \vec{k}') \\ \cos(\vec{k}, \vec{i}')\cos(\vec{k}, \vec{j}')\cos(\vec{k}, \vec{k}') \end{pmatrix}. $$

We will denote this matrix by $C(t)$. We can use the accelerometer to compute the initial value $C(0)$, using the orientation of the gravity vector and a chosen azimuth. The direct cosine matrix (DCM) will then be updated iteratively: the matrix

$$ B = \begin{pmatrix} 0 & -\partial \omega_z \partial t & \partial \omega_y \partial t \\ \partial \omega_z \partial t & 0 & -\partial \omega_x \partial t \\ -\partial \omega_y \partial t & \partial \omega_x \partial t & 0 \end{pmatrix} $$

is computed, with

$$ \vec{\omega}(t) = \begin{pmatrix} \omega_x(t) \\ \omega_y(t) \\ \omega_z(t) \end{pmatrix} $$

the output of the gyroscope. $B$ represents an infinitesimal 3-D rotation. Finally, letting $\sigma = \|\partial t \vec{\omega}\|$, we can use the Taylor expansion of the exponential to get

$$ C(t+\partial t) = C(t) \left( I + \frac{\sin \sigma}{\sigma} B + \frac{1-\cos \sigma}{\sigma^2} B^2 \right). $$

This allows us to compute the DCM iteratively.

#### D. Ultrasonic sensors

The SEES prototype of stick contains eight MaxBotix LV-MaxSonar®-EZ™ ultrasonic sensors (MaxBotix LV-MaxSonar®-EZ™). They are able to detect objects located between 0 and 6.45 meters with 2.54 cm centimetres accuracy from 15.24 cm centimetres [15]. We used these sensors with two goals in mind: the detection of obstacles for the user's safety (obstacle avoidance), and the adjustment of the computed position of the SEES during navigation. In this paper, we will focus on the latter use. We have designed a sensor model for the ultrasonic sensors: it works by comparing the theoretical measured distance if the stick was at a given position to the actual measured distance. The theoretical measured distance is computed using the map and a position and an orientation for the stick. Circle sectors corresponding to the shape of the ultrasonic beam pattern of each sensor are approximated using several rays; the model finds the ray finding an obstacle at the smallest distance; this distance is the theoretical measured distance. It should be observed that this sensor model works under the assumption that obstacles are signalled on the map. For this reason, the method has been designed with corridors in mind, which present normally relatively unencumbered flat surfaces.
## E. Camera

A Logitech HD Webcam C615 was used to capture the images in indoor space. This camera provides auto-focus and automatic reduction functions for object detection.

A Combined detecting method has been developed in this thesis to detect the door. Filtered HSV colour area and edge line of the potential objects were both considered in this combined detecting method. To reduce computing time, the source image (640 x 480) was down scaled to 40 x 30 pixels. Due to the good distribution of HSV colour space, a colour-based method with 6 HSV thresholds was implemented to detect a rough segment of the potential door. If the detected colour area was larger than a upper threshold, then the segment was believed to be a door. But if the detected colour area was smaller than a lower threshold, it would be ignored. Mostly, the area was between the lower threshold and the upper threshold, in this case, the Canny edge detection method and Hough Line Transform method were implemented to detect the lines that located around the four sides of the segment. With a basic confidence given by the colour area, the more lines there were, the larger was the final confidence, and more possible that the segment was considered as a door.

A multi-resolution pyramid algorithm was implemented for door handle and doorplate detection, which would only happen when the VIP has reached his destination. Accuracy was more important than a fast detection in this situation. The segment given by the door detection was relocated and a new segment under a higher solution, for example, the original 640\*480 image, was took as the input of the detections. After the same colour based method as door detection, new segments of the potential door handle and doorplate were detected. Then the Oriented FAST and Rotated BRIEF (ORB) [17] feature detector was implemented here, after a comparison with a pre-trained object database, the door handle and doorplate were detected.

# IV. EXPERIMENTS AND RESULTS

## A. Gyroscope and wheel encoder

A sample trajectory is shown in Fig. 2. It is taken from the website of the SMIR, where the SEES is sending its position data. The gyroscope was sampled at a frequency of 95 Hz, and the program used this data at a frequency of about 30 Hz. During this trajectory, a turn in the room E000 was performed to check more precisely the capacity of the gyroscope.

The result shown is quite good. The only major error is the crossing of the wall when entering the room; but this error is

likely to find its source in an error in the initial position, given that

- the angle is good when the room is entered

- the encoder cannot report a longer distance than what it actually travelled

However the error drift problem still holds; when doing more turns, the error on the integrated angle grows and becomes gradually too high for our application.

<figure><img src="image_3.png" /><figcaption>Fig. 3: Accelerometer signal analysis: raw signal is on the bottom row, then rectified, then integrated twice. The columns denote the three x-, y-, z- axes.</figcaption></figure>

## B. Accelerometer

A simple test of the accelerometer is shown on Fig. 3.

This test ran for approximately 14.27 s, the gyroscope was running at 95 Hz and the accelerometer at 100 Hz with a maximum range of ± 4 g. During the test, the stick was simply pushed and firmly stopped. Scilab 5.5.1 (Scilab Enterprises) was used to compute and draw the different curves.

At least four types of error are visible on this simple sample. The first type is the gain error. Every axis of the accelerometer has its own gain, which is only estimated in the datasheet. We have tried to estimate more precisely this gain: at rest, we measure the value of g and use it as a reference for the three axis. However, without a precise mean of calibrating independently each axis, we cannot compute precise per-axis gain. On our result, it looks like the z-axis has an underestimated gain, since after rectifying the gravity signal which comes mostly from the IMU z axis, we have a value that is higher than g. After two integrations, we see that the resulting position is 20 meters below the ground.

The second type is the gravity “overflow”. Due to errors in the attitude rectification, the room vertical axis is not perfectly collinear with the computed accelerometer vertical axis. Since we subtract the gravity vector norm to the computed vertical acceleration, any attitude mismatch will yield mistakes on all three axes. We can probably observe it in our sample on the y-axis: the acceleration measured there is not null even when at rest, yielding a strictly increasing velocity curve on the y-axis. It is also possible that this also comes from the gain miscalculation. We can mitigate this error by having a better estimate of the attitude of the accelerometer.

<figure><img src="image_4.png" /><figcaption>Fig. 2: A sample trajectory of the SEES</figcaption></figure>
The third error is the sensibility of the accelerometer to motions. We can see it on the y-axis, although it is not very obvious here. Before the motion, at rest, the output measure of acceleration is quite higher than 0; after the motion, at rest again, this measure is still positive but lower than before. We can check it by comparing the slopes of the velocity before and after the impulse: it is steeper before than after.

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4: The same signal with (bottom) and without (top) noise</figcaption></figure>

The fourth error cannot be seen on this sample, but is represented on the bottom curve of Fig. 4. It is a noise of unknown origin that affects the accelerometer of the LSM330DLC chip. It can be easily seen on the bottom acceleration curve of the figure: there are very high spikes in the signal. These singularities take a limited number of values, and are always directed the same way. This noise results in visible jumps in the integrated value (velocity), heavily deteriorating it – it takes a staircase shape. The number of spikes heavily depends on the parameterised sampling frequency of the accelerometer; it has not been observed at 10 Hz, it is rare at 100 Hz and very strong at 1 kHz. The constant values and this dependence on software settings makes us believe that it comes from the electronic signal processing part of the IMU chip. Correcting this noise requires non-trivial techniques given that it stays in the accelerometer's range (it can be mistaken for real signal), and has no clear frequency.

### C. Ultrasonic sensors

A sample visualisation of the ultrasonic sensor model is shown on Fig. 5. The grey and white line segments show the building map and the SEES stick. The detection beams from the ultrasonic sensors are also displayed on the figure, as

circle sectors around the SEES. The radii of these circle sectors are determined by the distances measured by the corresponding sensor. The pseudo-likelihoods outputs of the sensor model (i.e. probability of being at a location given the distances measured by the sensors) are represented by colours: warm colours (red, orange, yellow) denote high likelihoods while cold colours (green, blue) denote low likelihoods of being at a given position.

<figure><img src="image_7.png" /><figcaption>Fig. 5: Ultrasonic sensor model. Colours denote the likelihood of a position.</figcaption></figure>

The ultrasonic sensor model appears to offer a good complement to the conjoint use of the gyroscope and wheel encoder. Indeed, it does not suffer from drift, being an absolute positioning method; it seems to have a good precision that could be used for precise local position correction. However it suffers from a few problems: first of all is its computational weight. Let us denote the number of ultrasonic sensors by *k*, the number of rays used to simulate one circular sector by *n* and the number of walls by *p*. Our naive algorithm needs at least *n* × *p* × *k* operations to perform his computation, which is a lot; in particular with respect to the number of walls, which is potentially very high. Optimisations could be found, by only considering local walls for starters.

The second problem comes from the sensors themselves. Ultrasonic sensors emit an ultrasonic wave and measure the time before it returns to them to measure distances. This technique has downsides: first, the sound can bounce several times before returning to them. This gives us very poor results in complex environments that contains objects and when the sensor is not orthogonal to the obstacle – unfortunately, this is the case of most environments. Second, sensors can interfere with each other, when the sound from one sensor is received by another. This limits the environmental precision we can achieve, since we have to limit the number of sensors working at the same time (i.e. either less sensors more frequently or more sensors more rarely).

The third problem stems from the map. Since the sensor model relies on it to estimate the theoretical distances to obstacles, it cannot take into account what is actually around (e.g. furniture, people). Any object that is not represented on the map will then cause errors in the sensor model.
## D. Camera

<figure><img src="image_8.png" /><figcaption>Fig. 6: Door detection of SEES</figcaption></figure>

The object detection experiment had been done in building E, ISIMA. For door detection, with a Raspberry Pi board, the computing time was about 40 ms. The result is shown in Fig. 6. For door detection, the problem is to find good settings for the HSV colour thresholds. If the thresholds are too small, then the system might make a mistake and consider an object in a similar colour as a “door”. But if the thresholds are too large, the detecting distance might be less than 1 m, and doors could only be detected in the front of the camera, which is unacceptable for the SEES Project. The door detection algorithm should be accurate and reliable, which means it can not only help the VIP find the door correctly, but also assist the SEES system in locating the position of the VIP. This was a balance between the accuracy and the detecting distance. The proper colour thresholds could be trained by an GUI (Graphical User Interface) application.

For door handle detection, the computing time was about 200 ms and the result was shown in Fig. 7.

<figure><img src="image_9.png" /><figcaption>Fig. 7: Door handle detection with the SEES</figcaption></figure>

For doorplate detection, SIFT[16], SURF[17] and ORB[18] detection were compared. Given the same source image, SIFT detected 7 good matches both in PC platform and on the Raspberry Pi board, the cost time of the detection was 0.278 seconds in PC platform and 7.58 seconds in Raspberry board. SURF detected 2 good matches both in PC platform and on the Raspberry Pi board; the cost time for each detection was 0.140 second in PC platform and 2.07 in Raspberry Pi board. ORB detected 26 good matches, the cost time in PC platform

was 0.040 seconds and 0.6 seconds in the Raspberry Pi board. The comparison of time cost is shown in Fig. 8.

## Cost time of SIFT, SURF and ORB

<figure><img src="image_10.png" /><figcaption>Fig. 8: Cost time of SIFT, SURF and ORB feature detectors</figcaption></figure>

According to the computing time and number of matches, ORB feature detector was chosen in the SEES. The result of ORB feature detection is shown in Fig. 9.

<figure><img src="image_11.png" /><figcaption>Fig. 9: Doorplate detection using the ORB feature detector</figcaption></figure>

## V. CONCLUSION

In this paper, we have studied different sensors in the contexts of indoor navigation and environment recognition methods for the SEES project. Wheel encoder, accelerometer, gyroscope and ultrasonic sensors were used for the indoor navigation part. From the result given by the Raspberry Pi board, the path computation was quite good, except a light error of crossing the wall when entering the room; however, the accumulation of errors prevents the exclusive use of a naive method. Ultrasonic measurements appear to contain meaningful information to prevent this accumulation. A data fusion method is thus required to achieve the needed robustness and precision. The environment detection part was done with the use of the camera. It was used to recognise the objects such as door, door handle and doorplate in indoor space. The accuracy of the door detection was good and the
computing time was acceptable, but the door handle and doorplate detection still cost too much time.

For the future work, we might focus on improving the accuracy of indoor path computation and the reducing the cost time of door handle and doorplate detection.

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