Visual Slam featured image
Simultaneous Localization and Mapping (SLAM) has always been a hot topic in robotics and related fields. Exceptionally reliable technologies and solutions have...

Visual SLAM: Possibilities, Challenges and the Future

1. Introduction 

Simultaneous Localization and Mapping (SLAM) has always been a hot topic in robotics and related fields. Exceptionally reliable technologies and solutions have evolved over the decades of research and development, yet it is still considered to be an unsolved problem. 

There are many reasons, including the ever-growing applications of robotics that evolved from simple manipulators to complex robotics applications including self-driving cars. And for almost all those applications, a part of the problem to be solved is the same: The questions of where the robot is, what do my surroundings look like and how can I move around?  

SLAM tries to answer these questions, especially in the case of Autonomous Mobile Robots (AMRs). Over the years, many methods have been used to implement SLAM by using various sensors including 3D LiDAR’s, 2D Lidars, Radar, Stereo/RGB-D/Monocular Cameras etc. SLAM is often implemented with multiple sensors to reduce errors and increase accuracy. Each method that uses the above-mentioned techniques has its own advantages and disadvantages and a universal solution is not yet fully developed that can solve the issues in SLAM. 

2. LiDAR SLAM v/s Visual SLAM 

The two trending topics in SLAM are now Lidar based SLAM and Vision (Camera) based SLAM. The Lidar SLAM employs 2D or 3D Lidars to perform the Mapping and Localization of the robot while the Vison based / Visual SLAM uses cameras to achieve the same.  

LiDAR SLAM uses 2D or 3D LiDAR sensors to make the map and localize within it. Generally, 2D Lidar is used for indoor applications while 3D Lidar is used for outdoor applications. Being a more mature sensor technology LiDAR SLAM comes with its own advantages of being the most accurate SLAM Technology, thanks to the active sensor used and the sensor fusion algorithms.  

On the other hand, Visual SLAM uses vision-based sensors: Monocular, Stereo and RGB-D Cameras. These techniques find application in various robotics applications including both indoor and outdoor robotics use cases.  

While LiDAR SLAM is more reliable, precise and prone to fewer errors, it has its drawbacks: 

  • It demands a lot more compute power due to the type of data it handles 
  • The infrastructure cost for the LiDAR and the associated hardware is comparatively expensive at this point of time 
  • Other perception tasks including object detection and sign-board detection are much more complex  
  • Lack of semantic information 

This is where Visual SLAM algorithms get the spotlight. With cheaper hardware requirements and constantly improving algorithms, Visual SLAM is gaining more popularity and attention. The less compute requirement and the fact that the camera used for Visual SLAM can be used for other perception activities makes it a tempting choice in making autonomous robots with slow to medium speeds. And even the self-driving industry is utilizing the possibilities and applications of vision-based SLAM. 

3. Visual SLAM (VSLAM) : Is it the Way Forward? 

Vision sensors can exact more and viable information both in color and per pixel about location than any other sensor. Vision sensors are favored because people and animals seem to be navigating effectively in complicated locations using vision as a primary sensor. Various researchers have focussed on Visual Simultaneous Localization and Mapping (VSLAM) with exceptional results; however, many challenges still exist. 

In this blog series, we will be exploring the possibilities of Visual SLAM in robotics, by evaluating different V-SLAM techniques. We will discuss the possibilities of complex applications in terms of reliability, accuracy and efficiency of those techniques and algorithms. You will also find a bonus section (with a demo video) on one of the hottest Visual SLAM techniques, ORB SLAM algorithm. 

4. Types of VSLAM techniques 

 Visual sensors have been the main research direction for SLAM solutions because they are inexpensive, capable of collecting a large amount of information, and offer a large measurement range. The principle of VSLAM is simple, the objective is to estimate sequentially the camera motions depending on the perceived movements of pixels in the image sequence. This can be done in different ways. One approach is to detect and track some important points in the image; this is what we call Feature-based VSLAM. Another one is to use the entire image without extracting features; such an approach is called Direct SLAM. Of course, other SLAM solutions also exist using different cameras such as RGB-D or Time-of-Flight (ToF) cameras (which provide not only an image, but also the depth of the scene), or event cameras (detecting only changes in the image). 

Fig: Flowchart of a typical VSLAM System 

4.1 Feature-based SLAM

Feature-based SLAM can be divided again into two sub-families: filter-based, and Bundle Adjustment-based (BA) methods.  

While landmarks such as buildings and signposts are easily identified by humans, it is much easier for machines to identify and match low level features such as corners, edges, and blobs. More sophisticated feature definitions, together with detection algorithms and descriptors (a distinct feature representation) have been invented, such as Scale-invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB). These features are designed to be robust to translation, rotation, variations in scale, viewpoint, lighting, etc.   A limitation to the feature-based approach is that, once the features are extracted, all other information contained in the image is disregarded. This could be problematic when feature matching alone cannot offer robust reconstruction, e.g., in environments with too many or very few salient features, or with repetitive textures

4.2 Direct SLAM

In contrast to feature-based methods, direct methods directly use the image without any feature detectors and descriptors. Such feature-less approaches use photometric consistency to register two successive images (for feature-based approaches, the registration is based on the geometric positions of feature points). In this category, the most known methods are DTAM, LSD-SLAM, SVO, or DSO. Finally, with the development of deep learning, some SLAM applications have emerged to imitate the previously proposed approaches. Such research has generated semi-dense maps representing the environment, but direct SLAM approaches are time consuming and often require GPU-based processing.  

4.3.  RGB-D SLAM 

The structured light-based RGB-D camera sensors recently became inexpensive and small. Such cameras can provide 3D information in real-time but are used for indoor navigation as the range is inferior to four or five meters and the technology is extremely sensitive to sunlight. One can refer to RGB-D VSLAM approaches. 

4.4 Event Camera SLAM  

An event camera is a bio-inspired imaging sensor that can provide an “infinite” frame rate by detecting the visual “events,” i.e., the variations in the image. Such sensors have been recently used for V-SLAM. Nevertheless, this technology is not mature enough to be able to conclude about its performance for SLAM applications. 

5. Popular VSLAM Algorithms 

5.1 RTAB-Map SLAM  

RTAB-Map stands for Real-Time Appearance Based Mapping. It has been distributed as an open-source library since 2013. RTAB-Map started as an appearance-based loop closure detection approach with memory management (shown in below figure) to deal with the large-scale and long-term online operation. It then grew to implement Simultaneous Localization and Mapping (SLAM) on various robots and mobile platforms. RTAB-Map supports both visual and LiDAR SLAM, providing in one package a tool that allows users to implement and compare a variety of 3D and 2D solutions for a wide range of applications with different robots and sensors. It uses depth images with RGB images to construct maps. The graph is created here, where each node contains RGB and depth images with corresponding odometry pose. The links represent the transformations between nodes. When the graph is updated, RTAB-Map compares the new image with all previous ones in the graph to find a loop closure. When a loop closure is found, graph optimization is done to correct the poses in the graph. For each node in the graph, we generate a point cloud from the RGB and depth images. This point cloud is transformed using the pose in the node. The 3D map is then created. 

Where RTAB map can be used: 3D reconstruction  

RTAB map is more computationally intensive. So, optimizing it for a small-scale system would affect performance. Also, it can run only on RGB-D/ Stereo cameras and Lidars 

5.2 Deep Learning in VSLAM 

Geometry-based and Deep learning-based visual odometry paradigms.  

The geometry-based visual odometry computes the camera pose from the image by extracting and matching feature points.  

The deep learning-based visual odometry can estimate the camera pose directly from the data. For supervised visual odometry, it requires external ground truth as the supervision signal, which is usually expensive. In contrast, the unsupervised visual odometry uses its output as supervision signal. Besides, the local optimization module is optional for deep learning-based visual odometry. 

Fig. A Sample deep-learning based SLAM system architecture. 

5.3 ORB-SLAM  

ORB-SLAM is a real-time SLAM library for monocular, stereo and RGB-D cameras that computes the camera trajectory and a sparse 3D reconstruction. It can detect loops and re-localize the camera in real time. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoor sequences, to drones in industrial environments and cars driving around a city. The back end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. The system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches map points that allow for zero-drift localization. The main functionalities of ORB SLAM are feature tracking, mapping, loop closure and localization. 

Where ORB can be used:  

  •  Visual Odometry and Localization for Robots (indoor and outdoor) 

The recent update of ORB SLAM 3 is a big leap and shows great possibilities such as: 

  1. ORB-SLAM3 is the first system able to perform visual, visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras, using pinhole and fisheye lens models.  
  2. It is a feature-based tightly integrated visual-inertial SLAM system that fully relies on Maximum-aPosteriori (MAP) estimation, even during the IMU initialization phase. It is a system that operates robustly in real time, in small and large, indoor and outdoor environments, and is two to ten times more accurate than previous approaches.  
  3. ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information.  
  4. It has a multiple map system that relies on a new place recognition method with improved recall. ORB-SLAM3 can survive long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas. 

It has a multiple map system that relies on a new place recognition method with improved recall. ORB-SLAM3 can survive long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas.


With the advancements in computer vision and processing capabilities, VSLAM algorithms are on the path to greatness. Even though the environmental and optical conditions can affect performance, latest techniques and methods including sensor fusion and deep learning are showing light to the possibility of robots that need only ‘eyes’ to move around.  

ORB SLAM 3 is one of the most popular algorithms among VSLAM techniques. And in the next part of the blog, we will be diving deep into the ORB SLAM algorithm and its usability and capabilities.   

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Human Pose Detection & Classification

Some Buildings in a city


  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

  • Patient Monitoring in Hospitals
  • Surveillance
  • Sports/Exercise Pose Estimation
  • Retail Analytics

OCR / Pattern Recognition

Some Buildings in a city

Use cases :

  • Analog dial reading
  • Digital meter reading
  • Label recognition
  • Document OCR

Highlights :

  • Configurable for text or pattern recognition
  • Simultaneous Analog and Digital Dial reading
  • Lightweight implementation

Behavior Monitoring

Some Buildings in a city

Use cases :

  • Fall Detection
  • Social Distancing

Highlights :

  • Can define region of interest to monitor
  • Multi-subject monitoring
  • Multi-camera monitoring
  • Alarm triggers

Attire & PPE Detection

Some Buildings in a city

Use cases :

  • PPE Checks
  • Disallowed attire checks

Use cases :

  • Non-intrusive adherence checks
  • Customizable attire checks
  • Post-deployment trainable


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    Real Time Color Detection​

    Use cases :

    • Machine vision applications such as color sorter or food defect detection

    Highlights :

    • Color detection algorithm with real time performance
    • Detects as close to human vison as possible including color shade discrimination
    • GPGPU based algorithm on NVIDIA CUDA and Snapdragon Adreno GPU
    • Extremely low latency (a few 10s of milliseconds) for detection
    • Portable onto different hardware platforms

    Missing Artifact Detection

    Use cases :

    • Detection of missing components during various stages of manufacturing of industrial parts
    • Examples include : missing nuts and bolts, missing ridges, missing grooves on plastic and metal blocks

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Single-pass detection of many categories of missing artifacts
    • In-field trainable neural networks with dynamic addition of new artifact categories
    • Implementation using low cost cameras and not expensive machine-vision cameras
    • Learning via the use of minimal training sets
    • Options to implement the neural network on GPU or CPU based systems

    Real Time Manufacturing Line Inspection

    Use cases :

    • Detection of defects on the surface of manufactured goods (metal, plastic, glass, food, etc.)
    • Can be integrated into the overall automated QA infrastructure on an assembly line.

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Use of consumer or industrial grade cameras
    • Requires only a few hundred images during the training phase
    • Supports incremental training of the neural network with data augmentation
    • Allows implementation on low cost GPU or CPU based platforms

    Ground Based Infrastructure analytics

    Some Buildings in a city

    Use cases :

    • Rail tracks (public transport, mining, etc.)
    • Highways
    • Tunnels

    Highlights :

    • Analysis of video and images from 2D & 3D RGB camera sensors
    • Multi sensor support (X-ray, thermal, radar, etc.)
    • Detection of anomalies in peripheral areas of core infrastructure (Ex: vegetation or stones near rail tracks)

    Aerial Analytics

    Use cases :

    • Rail track defect detection
    • Tower defect detection: Structural analysis of Power
      transmission towers
    • infrastructure mapping

    Highlights :

    • Defect detection from a distance
    • Non-intrusive
    • Automatic video capture with perfectly centered ROI
    • No manual intervention is required by a pilot for
      camera positioning


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    Founder and Managing director of Ignitarium, Sanjay has been responsible for defining Ignitarium’s core values, which encompass the organisation’s approach towards clients, partners, and all internal stakeholders, and in establishing an innovation and value-driven organisational culture.


    Prior to founding Ignitarium in 2012, Sanjay spent the initial 22 years of his career with the VLSI and Systems Business unit at Wipro Technologies. In his formative years, Sanjay worked in diverse engineering roles in Electronic hardware design, ASIC design, and custom library development. Sanjay later handled a flagship – multi-million dollar, 600-engineer strong – Semiconductor & Embedded account owning complete Delivery and Business responsibility.


    Sanjay graduated in Electronics and Communication Engineering from College of Engineering, Trivandrum, and has a Postgraduate degree in Microelectronics from BITS Pilani.


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      Board Member

      Ramesh was the Founder and CEO of Insta Health Solutions, a software products company focused on providing complete hospital and clinic management solutions for hospitals and clinics in India, the Middle East, Southeast Asia, and Africa. He raised Series A funds from Inventus Capital and then subsequently sold the company to Practo Technologies, India. Post-sale, he held the role of SVP and Head of the Insta BU for 4 years. He has now retired from full-time employment and is working as a consultant and board member.


      Prior to Insta, Ramesh had a 25-year-long career at Wipro Technologies where he was the President of the $1B Telecom and Product Engineering Solutions business heading a team of 19,000 people with a truly global operations footprint. Among his other key roles at Wipro, he was a member of Wipro's Corporate Executive Council and was Chief Technology Officer.


      Ramesh is also an Independent Board Member of eMIDs Technologies, a $100M IT services company focused on the healthcare vertical with market presence in the US and India.


      Ramesh holds an M-Tech in Computer Science from IIT-Kanpur.


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      Prior to Ignitarium, Malavika has worked in with multiple global tech startups and IT consulting companies as a marketing consultant. Earlier, she headed marketing for the Semiconductor & Systems BU at Wipro Technologies and worked at IBM in their application software division.

      Malavika completed her MBA in Marketing from SCMHRD, Pune, and holds a B.E. degree in Telecommunications from RVCE, Bengaluru.



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      Pradeep graduated in Industrial Engineering and Management, went on to secure an MBA from CUSAT, and cleared UGN Net in Management. He also had teaching stints at his alma mater, CUSAT, and other management institutes like DCSMAT. A certified P3O (Portfolio, Program & Project Management) from the Office of Government Commerce, UK, Pradeep has been recognized for key contributions in the Management domain, at his previous organizations, Wipro & Virtusa.

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      Sona graduated in Commerce from St. Xaviers College and did her MBA in HR from PSG College of Technology.



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      Ashwin has worked with the likes of Wipro, GlobalLogic, and Mastek, wherein unconventional and creative business models were used to bring in non-linear revenue. He has strategically diversified, de-risked, and grown his portfolios during his sales career.


      Ashwin strongly believes in the customer-first approach and works to add value and enhance the experiences of our customers.


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      Vice President – Sales & Business Development

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      Azif handled key accounts and sales process initiatives at Sankalp Semiconductors. Azif has pursued entrepreneurial interests in the past and was associated with multiple start-ups in various executive roles. His start-up was successful in raising seed funds from Nokia, India. During his tenure at Nokia, he played a key role in driving product evangelism and customer success functions for the multimedia division.


      At Wipro, he was involved in customer engagement with global customers in APAC and US.


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      Distinguished Engineer – Digital

      At Ignitarium, Raju's charter is to architect world class Digital solutions at the confluence of Edge, Cloud and Analytics. Raju has over 25 years of experience in the field of Telecom, Mobility and Cloud. Prior to Ignitarium, he worked at Nokia India Pvt. Ltd. and Sasken Communication Technologies in various leadership positions and was responsible for the delivery of various developer platforms and products.


      Raju graduated in Electronics Engineering from Model Engineering College, Cochin and has an Executive Post Graduate Program (EPGP) in Strategy and Finance from IIM Kozhikode.


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      Prior to joining Ignitarium in 2017, Pradeep was Senior Solutions Architect at Open-Silicon, an ASIC design house. At Open-Silicon, where he spent a good five years, Pradeep was responsible for Front-end, FPGA, and embedded SW business development, marketing & technical sales and also drove the IoT R&D roadmap. Pradeep started his professional career in 2000 at Sasken, where he worked for 11 years, primarily as an embedded multimedia expert, and then went on to lead the Multimedia software IP team.

      Pradeep is a graduate in Electronics & Communication from RVCE, Bangalore.


      SUJEET SREENIVASAN Vice President – Embedded


      Vice President – Automotive Technology


      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.


      RAJIN RAVIMONY Distinguished Engineer


      Distinguished Engineer


      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.


      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.


      Rajin holds an MS in Micro-electronics from BITS Pilani.


      SIBY ABRAHAM Executive Vice President, Strategy


      Executive Vice President, Strategy


      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.


      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.


      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.


      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut


      SUJEETH JOSEPH Chief Product Officer


      Chief Technology Officer


      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.


      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.


      He graduated in Electronics Engineering from Bombay University in 1991.


      SUJITH MATHEW IYPE Co-founder & CTO


      Co-founder & COO


      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.


      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".


      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.


      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.


      RAMESH SHANMUGHAM Co-founder & COO


      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.