DUAL FOOT MOUNTED PEDESTRIAN NAVIGATION SYSTEM USING OBLU COLLEGE : B.M.S. INSTITUTE OF TECHNOLOGY AND MANAGEMENT, YELAHANKA, BENGALURU GUIDES : Prof. K.V.S. HARI Dr. A. SHOBHA RANI STUDENTS : Ms. TANYA KURUVILLA Mr. KOTHA AAKASH Ms. TANYA SINGH Mr. SANJAY J SHENOY INTRODUCTION There has been an exigent need for a navigation system that is robust, with accurate positioning system with seamless indoor and outdoor coverage that can increase the safety in emergency response and military operations. The most commonly used navigation system is the GPS that provides high accuracy in many situations. But, the main challenge is to create a navigation system that is sufficiently accurate in GPS denied environments. This is a major problem in certain situations, such as military and disaster relief operations, where one has to track the first responders who arrive at the scene to carry out their mission. Global positioning systems provide a range of navigation accuracies at very low cost and low power consumption. The devices that use GPS are both portable and are well suited for integration with other sensors, communication links, and databases. However, the need for alternative positioning system arises because GPS does not work in all environments, especially indoor environments. This is a major problem in situations such as military and disaster relief operations, where one has to track the first responders who arrive at the scene to carry out their mission. Over thepast several years, the need for tracking systemsin indoor environments has seen a sharp rise. In India, over 100,000 deaths occur annually due to fires in homes and workplaces. The number of fatalities has not changed significantly over the past 20 years despite smoke alarms, fire extinguishers and large efforts regarding information campaigns. Fire-fighters that enter buildings, which are on fire, experience very difficult conditions. The heat generated by the fire in combination with the weight of the personal protection
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DUAL FOOT MOUNTED PEDESTRIAN NAVIGATION
SYSTEM USING OBLU
COLLEGE : B.M.S. INSTITUTE OF TECHNOLOGY AND MANAGEMENT, YELAHANKA, BENGALURU GUIDES : Prof. K.V.S. HARI
Dr. A. SHOBHA RANI STUDENTS : Ms. TANYA KURUVILLA
Mr. KOTHA AAKASH Ms. TANYA SINGH Mr. SANJAY J SHENOY
INTRODUCTION
There has been an exigent need for a navigation system that is robust, with accurate
positioning system with seamless indoor and outdoor coverage that can increase the safety in
emergency response and military operations. The most commonly used navigation system is
the GPS that provides high accuracy in many situations. But, the main challenge is to create a
navigation system that is sufficiently accurate in GPS denied environments. This is a major
problem in certain situations, such as military and disaster relief operations, where one has to
track the first responders who arrive at the scene to carry out their mission.
Global positioning systems provide a range of navigation accuracies at very low cost and low
power consumption. The devices that use GPS are both portable and are well suited for
integration with other sensors, communication links, and databases. However, the need for
alternative positioning system arises because GPS does not work in all environments,
especially indoor environments. This is a major problem in situations such as military and
disaster relief operations, where one has to track the first responders who arrive at the scene
to carry out their mission. Over thepast several years, the need for tracking systemsin indoor
environments has seen a sharp rise. In India, over 100,000 deaths occur annually due to fires
in homes and workplaces. The number of fatalities has not changed significantly over the
past 20 years despite smoke alarms, fire extinguishers and large efforts regarding information
campaigns.
Fire-fighters that enter buildings, which are on fire, experience very difficult conditions. The
heat generated by the fire in combination with the weight of the personal protection
equipment and water hose may cause exhaustion. Combined with high stress levels in smoke-
filled or dark environments (which can be expected during power outages caused by the fire)
there is an apparent risk for disorientation. Hence, there is a significant risk due to the
inability to correctly describe their movements to their supervisor. Depending on the role,
fire-fighters have different information requirements. For the smoke-divers, any localization
system will primarily increase their safety; however, for the sector chief, the technology
provides a means to rapidly comprehend whatthe situation is like and how to deploy
resources, especially in large incidents with multiple fire stations deployed at the scene.
Automatic mapping capability is an important complement to the localization technology,
since fire-fighters are not expected to have access to building floor-plans. While law
enforcement officers, fire-fighters, and military personnel have varying requirements for
localization and tracking systems, all three groups share certain keyrequirements. An
accurate positioning system could enable an alarm functionality which could prevent these
life threatening situations. For instance, this system can be used by smoke-divers and it will
increase their safety and provide a means to quickly understand what the situation is like and
how to deploy resources, especially in large incidents. This increases the need for a robust
and accurate positioning system that works in indoor and outdoor environments.
The key to achieving a system with good accuracy during indoor operations is to use
appropriate positioning sensors. One such system that provides good accuracy is the
OpenShoe, a real-time, embedded implementation of a foot mounted, zero-velocity aided
INS. An accurate positioning system could enable an alarm functionality which could
prevent these life-threatening situations where fire-fighters get lost. According to a survey
carried out by National Fire Protection Association (NFPA) one-third of the firefighting
fatalities of the fire-fighters occurred on the fire ground. This can be dramatically changed by
the use of an INS-based shoe. In addition, other applications like location of elderly in care-
centers, and of employees in an organization are some scenarios where indoor positioning is
required and has seen increasing demand.
Chapter 2
LITERATURE SURVEY
The need for alternative positioning system arises because GPS does not work in all
environments, especially indoor environments.This is a major problem in certain situations,
such as military and disaster relief operations. Over the past several years, the need for
tracking systems in indoor environments has seen a sharp rise.There are several technologies
which are being used for indoor positioning. Some of them are based on Wi-Fi or Ultra-
WideBand (UWB). These technologies assume an infrastructure like a Wi-Fi network or a
UWB network being available in the area of interest. The accuracies provided by these
technologies range from a few cms to a few meters.In the case of harsh environments like a
disaster-affected building, such assumptions of infrastructure cannot be made. Therefore,
there is a need for developing autonomous positioning systems.
Inertial Navigation System (INS) technology is capable of working in almost all
environments where GPS has difficulties, Microelectromechanical systems (MEMS) inertial
technology is seen as both a possible complement of GPS technology and a potential
alternative to GPS. INSs can provide position information whenever GPS signals are
unavailable (in tunnels, indoors, underground facilities), ensuring a possible seamless
provision of position information.
A foot-mounted INS, with low-quality inertial sensors, will not work if it is not fused with
other information when the subject is on a moving platform such as a train or vehicle.
Fortunately, both systems can provide information, which can be fused in an optimal manner
to obtain good accuracies in the position estimates.
A block diagram of a strap-down INS is shown.
Fig2.1:Block Diagram of strap down INS
The INS comprises the following two distinct parts, the Inertial Measurement Unit (IMU)
and the computational unit. The former provides information on the accelerations and
angular velocities of the navigation platform relative to the inertial coordinate frame of
reference. The angular rotation rates observed by the gyroscopes are used to track the relation
between navigation platform co-ordinate system and the navigation coordinate frame. This
information is then used to transform the specific force observed innavigation platform
coordinates into the navigation frame, where the gravity force is subtracted from the
observed specific force. The accelerations in the navigation coordinates are integrated twice,
with respect to time, to obtain the position of the navigation platform.
The navigation calculations in INS involve integration with time, which provide a low-pass
filter characteristic that suppresses high-frequency sensor errors but amplifies low-frequency
sensor errors and initialization errors. This results in a position error that grows without
bound as a function of the operation time and where the error growth depends on the error
characteristics of the sensors and the initialization error. In general, it holds that for a low-
cost INS, a bias in the accelerometer measurements causes position error growth that is
proportional to the square of the operation time, and a bias in the gyroscopes causes position
error growth that is proportional to the cube of the operation time (due to an extra integration
to obtain the relative angle between the navigation frame and body frame). The detrimental
effect of gyroscope errors on the navigation solution is due to the direct reflections of the
errors on the estimated attitude. The attitudeis used to calculate the current gravity force in
navigation coordinates and cancel its effect on the accelerometer measurements. The errors
in the cancellation of the gravity acceleration are then accumulated in the velocity and
position calculations. For a low-cost INS using gyroscopes with a bias on the order of 0.01
[°/s] this means that the position error is more than 10 [m] already after 10 [s] of operation.
A navigation system which has such an error growth, is basically useless for indoor
navigation. However, by utilizing the fact that an INS mounted on the foot of the user
regularly becomes stationary, i.e., has zero instantaneous velocity, the errors in the INS can
be estimated and partly compensated for, and the devastating cubical error growth can be
mitigated.
Fig 2.2: Block Diagram of zero-velocity aided INS
The block diagram of zero-velocity aided INS is shown. The zero-velocity aided INS
comprises the following three distinct parts, the INS, the zero-velocity detector and the
Kalman filter; the Kalman filter has a state-space model describing how the errors in INS
develop with time.3 The INS works as the backbone of the system, continuously estimating
the navigation state of the system. Whenever the zero-velocity detector detects that the
system is stationary (close to zero instantaneous velocity), this information is used as an
input to the Kalman filter that estimates the errors in the estimated navigation state. The
estimated errors are used to correct (calibrate) the internal states of the INS.
The detection of the zero-velocity events can be done using external force sensors or radar
sensors when the shoe is in contact with the ground. However, external force sensors are
prone to mechanical fatigue and may fail to detect that the foot is stationary in situations such
as when the user sits down and does not apply his weight on the shoe.
Fig 2.3: IMU sensor in shoe
Radar sensors require costlyelectronics and they cannot detect zero-velocity events if the
radar (sole) is not directed towards the ground, e.g. when the user is crawling. Therefore, the
zero-velocity events are generally detected directly from the IMU data, assuming that when
the foot-mounted INS is stationary, the angular rate of the system is zero and the specific
force vector is constant with a magnitude equal to the gravity force.
Chapter 3
HARDWARE REQUIREMENTS
3.1 Oblu
Oblu is an open source motion sensing platform for wearables and robots. It comes pre-
programmed as a shoe sensor for pedestrian navigation. It finds applications in industrial
safety and resource management, assistive robotics, gaming and Geo-survey of GPS devoid
area.Oblu is compatible with Arduino, Raspberry Pi and any other generic development
board. Under normal operating conditions, Oblu draws ~ 100 mA to 110 mA of current at an
operating voltage range of 4.2 V to 3.2 V when connected to battery. Therefore minimum
power consumption by Oblu is ~350 mW. Any generic USB data cable can be used to
program Oblu. It is possible to add add sensors, IMUs, GPS, Wi-Fi, UWB or ZigBee with
Oblu. Oblu can communicate reliably via BLE for a range of 2 meters.
Fig 3.1:Oblu
3.1.1 Block Diagram of Oblu
Fig 3.2: Oblu Block Diagram
3.1.2 Features of Oblu
The highlighting features are
1. Four 6-axis IMUs (3-axis Accelerometer + 3-axis Gyroscope):
a. Accelerometer range: ±2g, ±4g, ±8g, ±16g
b. Gyroscope range: ±250, ±500, ±1000, ±2000 deg/s
c. Max. sampling rate: 1 KHz
2. USB (Bootloader) programmable
3. JTAG programming interfaces for main controller & BLE
4. IMUs‟ placement in 2x2 array form
5. 32-bits floating pt controller with 512 Kb internal flash
6. Bluetooth Low Energy (BLE) 4.1 & USB 2.0 interfaces
7. Access to UART, SPI, I2C and GPIO pins
8. Access to ADC input of BLE (for battery indication)
9. Access to 3.3 V, 5 V, GND & RESET
10. Powering with Li-ion battery & USB, battery charging through USB
11. Max current: 100 mA
12. Onboard LED indicators; Reset button
13. Dimensions: 35.0 x 25.9 x 11.8 mm
14. Battery connector for easy attachment
15. Backup of 5 hrs 30 mins with 500mAH battery
Common Switched and Connectors
Bootloading Switch S1
Reset Switch S2
Test LED/ BT pairing LED1
Batt. Charging Status LED2
Board Power-up LED3
J1
Fig 3.3:Oblu Pin Configuration
PD00 1 VBAT 2
PD02 3 P1.6 4
PD03 5 P0.7 6
PD01 7 P0.4 8
5V 9 P0.5 10
PC03 11 P3.4 12
PC02 13 PD12/P1.5 14
IMU_INT 15 PD11/P1.4 16
J2
3.3V 1
GND 2
RST_N/ XRES 3
SWDCLK/ SCLK 4
SWDIO 5
IMU Chart
IMU#N IN
J3
PB01 1
PA19 2
PA16 3
PA09 4
PA08 5
3.3V 6
RST_N 7
PA00/TCK 8
PA01/TD1 9
PA02/TD0 10
PA03/TMS 11
GND 12
Fig 3.4:Oblu JTAG Interface
3.1.3 Oblu Microcontroller AT32UC3C
The AT32UC3C is a complete System-On-Chip microcontroller based on the AVR32UC
RISC processor running at frequencies up to 66 MHz. AVR32UC is a high-performance 32-
bit RISC microprocessor core, designed for cost-sensitive embedded applications, with
particular emphasis on low power consumption, high code density and high performance.
The processor implements a Memory Protection Unit (MPU) and a fast and flexible interrupt
controller for supporting modern operating systems and real-time operating systems. Using
the Secure Access Unit (SAU) together with the MPU provides the required security and
integrity.
The AT32UC3C incorporates on-chip Flash and SRAM memories for secure and fast access.
For applications requiring additional memory, an external memory interface is provided on
AT32UC3C0 derivatives.
Fig 3.5: AT32UC3C
3.1.4 Features of Microcontroller AT32UC3C
• High Performance, Low Power 32-bit AVR Microcontroller
– Compact Single-cycle RISC Instruction Set Including DSP Instruction Set
– Built-in Floating-Point Processing Unit (FPU)
– Read-Modify-Write Instructions and Atomic Bit Manipulation
– Performing 1.49 DMIPS / MHz
• Up to 91 DMIPS Running at 66 MHz from Flash (1 Wait-State)
• Up to 49 DMIPS Running at 33 MHz from Flash (0 Wait-State)
– Memory Protection Unit
• Multi-hierarchy Bus System
– High-Performance Data Transfers on Separate Buses for Increased Performance
– 16 Peripheral DMA Channels Improves Speed for Peripheral Communication