Sensor Fusion, also known as multi-sensor, is the art of integrating multiple physical
sensors to enhance the application and system performance. Sensors are now
deployed in a broad spectrum of applications, such as smart mobile devices,
automotive systems, healthcare, oil exploration, climate monitoring, and
industrial controls. Sensors are used everywhere, and now the advancement in
sensor technology is enabling them to mimic human beings. This is possible now
because of sensor fusion technology. Sensor fusion leverages a microcontroller
to combine the individual data collected from multiple sensors to get a more
reliable and precise overview of the data. Sensor fusion enables context
awareness, which, in turn, can extensively benefit the Internet of Things
(IoT).
Sensor
fusion is the ability to bring together input from multiple radars, lidars, and
cameras to form a single model or a picture of the vehicle’s surrounding. The
resulting model is an accurate representation as it balances the strengths of
different sensors. Sensor fusion dynamically improves the lane detection
performance as more sensors are used to boost the perception capability. Sensor
fusion collects data from each sensor used through software algorithms to offer
the most comprehensive and accurate environmental model. The escalating use of
sensor fusion in consumer electronics and automotive applications is propelling
the sensor fusion market’s growth. According to Emergen Research, the Global Sensor
Fusion Market is forecast to attain a market valuation of USD 16.72 Billion by
2027, registering a remarkable CAGR of 19.6%.
Importance
and Working of Sensor Fusion Algorithms
Sensor
fusion algorithms integrate real-time sensor data that assists in reducing doubts
in the object’s location and position. They combine data from several sensors
to estimate the correct positions of the objects. Sensor fusions mostly rely on
data from numerous same types of sensors, also known as “competitive
configuration”. However, when merging data from different types of sensors,
such as amalgamation of object proximity data and speedometer data, more often
results in an in-depth interpretation of the object examined.
For
instance, during a foggy climate, a radar sensor offers more precise data than
a LiDAR sensor would. Whereas, in clear weather, LiDAR sensors’ spatial
resolution is much more reliable than radar sensor. Every sensor has its own
pros and cons, which is why sensor fusion algorithms take into consideration
multiple types of sensors. The data from these various sensors are
complementary, and the setup is typically called a complementary configuration.
Due
to each sensor’s strengths and weaknesses, a robust algorithm gives preference
to some critical data points over others. For instance, speed sensors are more
precise than parking sensors, so those sensors are mostly preferred. The
importance varies, and they generally depend on the specific use.
Sensor
fusion algorithms examine all the input data and generate output with enhanced
accuracy and reliability. To measure the kinematic state of a vehicle, two
equations and models are applied ‒ namely predict equation that used motion
model and update equation that uses the measurement model. The motion model
gives data about an object in periodic intervals, and the measurement type is
more involved with the vehicle sensors’ dynamics. One of the essential
foundations of these algorithms is the Kalman filter.
· Overview
of Kalman Filter
A
Kalman filter refers to an equation that obtains data inputs from numerous
sources and predicts unknown values, even in the case of augmented signal
noise. Majorly applicable in control and navigation tech, Kalman filters confer
the prediction of uncertain values more precisely than specific predictions by
single measurement method.
As
these algorithms are the most commonly used sensor fusion application and
provide a resilient foundation for the concept, sensor fusion and Kalman
filters are generally considered synonymous. Kalman filters are one of the most
popular algorithms in sensor fusion and were invented by Rudolph Kalman in
1960. Now the algorithm is widely deployed in smartphones and satellites from
navigation and tracking.
Factors
Influencing the Growth of Sensor Fusion Market
The
escalating trend of autonomous vehicle and advanced driver assistance systems
(ADAS) are further augmenting the need for new radar, GNSS, lidar, and camera
sensors in the vehicles. The increasing penetration of smartphones globally and
the augmenting trend of micro-sized electronics are anticipated to add to
traction to the market’s growth. Moreover, the growing application of fusion
technologies to survey and predict environmental conditions, such as
temperature, pressure, and humidity, have created an added demand for sensor
fusion technologies.
The
augmenting need for application-based location detection is attracting
consumers and investors to invest in the technology and deploy them across the
globe, thereby contributing to the market growth. The increasing advancement in
ADAS and the surging use of the GPS-Inertial Measurement Unit fusion is highly
beneficial for solving mounting errors. For instance, Tesla’s Autopilot
automated driving feature, which is an example of ADAS, has the ability to carry
out operations like maintaining the lane of the vehicle on a highway by
predicting the accurate location of the vehicle from the data obtained from a
forward-facing camera and steering control.
Collaborations
and agreements are a common occurrence in the market and are primarily
contributing to the market’s expansion. For instance, in April 2020, Foresight
Autonomous Holdings Ltd., an Israel-based leader in automotive vision systems,
joined forces with FLIR Systems Inc., to develop and commercialize the former’s
QuadSight Vision System, which is based on sensor fusion, combined with infrared
cameras of FLIR Systems to a broad spectrum of customers. In 2019, Ceva Inc.
gained a controlling stake in the smart sensor technology business of Hillcrest
Lab. Under the acquisition, CEVA’s intelligent sensing technology’s portfolio
has the chance to expand to include CPU vision and AI processing for cameras
and sound processing for microphones.
In
December 2020, Himax Technologies Inc., joined forces with Edge Impulse to
enable the accelerated development and deployment of the machine learning
model. The AI vision and sensor fusion solution are designed for predictive
maintenance, asset tracking, occupancy detection, and condition monitoring.
STMicroelectronics
and Mobileye have formulated a strategy to develop EyeQ 5 system on chip in
association with each other, to be deployed in 2020, to perform the function of
central computer control and sensor fusion in the fully autonomous driving
vehicles. Companies actively focused on developing technologies linked with IoT
and smart driving are anticipated to create lucrative future opportunities. The
increasing demand for monitoring and controlling utilities in home automation
and the escalating development in robotics is forecast to influence the market
growth due to sensor fusion technology’s wide application in mobile robot
navigation.
