VSLAM Loop Closure photo
In the third installment of our Visual SLAM series, we delve into the pivotal concept of loop closure.

VSLAM Series - Loop Closure Detection


In the third installment of our Visual SLAM series, we delve into the pivotal concept of loop closure. Building upon the foundational understanding laid out in the previous articles, we now embark on a journey to explore how loop closure enhances the robustness and accuracy of Simultaneous Localization and Mapping (SLAM) systems.

As we navigate through this crucial aspect of visual SLAM technology, we unravel its significance in enabling autonomous navigation, mapping, and localization in dynamic environments. For a better understanding of the content, check out the first and second part of this blog series that provides a comprehensive study of various aspects of visual SLAM.

Understanding Loop Closure

At its core, loop closure refers to the identification of previously visited locations in a robot’s trajectory. Imagine a scenario where a robot explores an environment, capturing visual data along its path. Loop closure detection enables the system to recognize when the robot revisits a location it has encountered before, thereby closing the loop in its trajectory. This capability is fundamental for accurate map building and localization, as it helps mitigate drift errors that accumulate over time.

Imagine you’re tasked with putting together a giant jigsaw puzzle, but you can only see a few pieces at a time. This is similar to how Visual SLAM (Simultaneous Localization and Mapping) works initially. The “frontend” quickly analyzes small sections of the environment, identifying key features like landmarks and estimating short-term trajectories. This provides a basic starting point for the map, but it is still like working with just a few puzzle pieces.

The “backend” of SLAM takes over to refine this initial picture. It acts like a meticulous puzzler, trying to fit everything together seamlessly. However, if the backend relies solely on connections between neighboring pieces (like in Visual Odometry), errors creep in over time. Each connection introduces a small inaccuracy, and these errors build upon each other like dominoes falling. This leads to a distorted final map, just like the puzzle wouldn’t be complete or accurate if you only considered connections between adjacent pieces.

To create a truly accurate and consistent map, SLAM needs a way to recognize when it has revisited a familiar location. This is like realizing you’ve stumbled upon the same exact corner piece of the puzzle multiple times while exploring different sections. This information, known as loop closure, is crucial for the backend. It allows SLAM to establish global consistency, ensuring that all the puzzle pieces eventually fit together perfectly, and the final map accurately reflects the entire environment. 

Check out the illustration below to see VSLAM loop closure in action.

SLAM bit

This loop closure capability serves two key purposes:

  • Improved Accuracy:
    Loop detection helps correct errors that accumulate over time in the estimated trajectory and map. By recognizing familiar locations, the system can adjust its position and refine the overall map, leading to a more accurate representation of the environment.

  • Enhanced Robustness:
    Loop detection enables a powerful feature called relocation. Imagine recording a reference path for a robot beforehand. With relocation, the robot can determine its position on this pre-recorded track, even if it encounters unexpected obstacles or sensor errors. This significantly improves the system’s ability to navigate reliably.

The Impact of Loop Closure on SLAM Maps

(a) Without Loop Closure:

This image shows a map created using a monocular vision-based SLAM system (one camera). The inner curve represents the robot’s path, and the outer points represent the mapped features. Notice how the path appears to drift away from the actual trajectory. This is because errors in estimating the robot’s movement accumulate over time without loop closure.

(b) With Loop Closure:

This image demonstrates the significant improvement achieved by using loop closure in the same SLAM system. The robot’s path (inner curve) is much straighter and aligns better with the actual trajectory. The map features (outer points) are also more tightly clustered and likely represent the environment more accurately. Loop closure helps the system recognize previously visited locations, correct accumulated errors, and generate a more consistent and reliable map.

Implementing Loop Closure

When considering the implementation of loop closure detection in visual SLAM systems, various theoretical and engineering approaches come into play, each with its own advantages and limitations. One straightforward method involves performing feature matching on image pairs to determine relatedness based on the number of correct matches. While simple and effective, this approach assumes that any two images could potentially form a loop, resulting in a prohibitively large number of comparisons, especially as the trajectory lengthens, making it impractical for real-time systems.

Another approach involves randomly selecting historical data and performing loop detection on subsets of frames. While this maintains constant calculation time, as the number of frames increases, the probability of detecting a loop decreases, leading to reduced detection efficiency.

To refine these coarse methods, more sophisticated approaches consider predictive cues to narrow down potential loop candidates. These can be broadly categorized into odometry-based or appearance-based methods.

Odometry-based methods rely on geometric relationships, detecting loop closures when the current camera position closely resembles a previous position. However, estimating accumulated drift accurately is challenging without global position measurements like GPS, and this approach assumes small cumulative errors, rendering it ineffective when errors accumulate significantly.

On the other hand, appearance-based methods focus solely on image similarity to determine loop closure relationships, independent of frontend or backend estimation. This approach eliminates accumulated errors and has become the mainstream method in visual SLAM systems. By calculating the similarity score between images, loop closures can be identified effectively across various scenarios.

However, determining image similarity is not straightforward due to factors like perceptual aliasing and variability. Directly subtracting pixel values between images often yields unrealistic differences due to environmental factors like lighting changes and viewpoint variations. Consequently, defining a function to accurately reflect image similarity becomes crucial, considering perceptual aliasing and variability.

Precision and Recall

From a human perspective, determining whether two images were taken from the same place or are similar is intuitive, yet articulating precisely how our brains achieve this remains elusive. However, in the realm of computer programs, we aim for algorithms to make judgments consistent with human perception or objective facts. Just like in machine learning, where algorithms may not always align with human thinking, loop detection algorithms may yield four possible outcomes: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).

These terms describe perceptual bias (false positives) and perceptual variation (false negatives). To evaluate loop detection algorithms, we employ two key statistics: accuracy rate and recall rate (also known as precision and recall). Accuracy rate measures the likelihood that all loops detected by the algorithm are true, while recall rate indicates the probability of detecting all real loops.

Since algorithms often have multiple parameters, such as thresholds, adjusting these parameters affects the trade-off between accuracy and recall. A higher threshold may improve accuracy by reducing false positives but could lead to missed real loops, lowering recall. Conversely, a lower threshold increases recall but might introduce more false positives, decreasing accuracy.

To assess algorithm quality, precision-recall curves are constructed, mapping recall rate against accuracy rate. The curve’s shape indicates the algorithm’s performance across different parameter configurations. In SLAM, where accuracy is paramount, we prioritize strict parameters or include loop verification steps to mitigate false positives. While lower recall may result in missed loops and accumulated errors, remaining loops could correct them.

Returning to the question of why we don’t directly use image differencing (A – B) to calculate similarity, empirical evidence shows that its accuracy and recall fall short compared to current methods, leading to numerous false positives or negatives. Thus, we opt for more sophisticated techniques that better capture image similarity and align with the precision and recall requirements of loop closure detection in visual SLAM systems.

Bag Of Words

The Bag-of-Words (BoW) [briefly explained in the first blog of this series] concept aims to describe an image in terms of the types of features present within it. For instance, consider two images—one containing a person and a car, and the other featuring two people and a dog. By encoding these features into a description, we can measure the similarity between the images. This process involves:

  1. Defining concepts like “person,” “car,” and “dog” as words, which are compiled into a dictionary.
  2. Detecting which predefined words from the dictionary appear in an image. The appearance of these words is represented by a histogram, providing a vector description of the entire image.
  3. Computing similarity between images based on the histograms generated in the previous step.


In conclusion, loop closure detection is the cornerstone of robust and accurate visual SLAM. By recognizing revisits to previously explored areas, it corrects errors that accumulate over time and allows the system to build a globally consistent map. This enhanced map, coupled with the ability to relocate using pre-recorded paths, paves the way for reliable and dependable robot navigation in complex environments. As research in loop closure detection continues to advance, we can expect even more sophisticated SLAM systems capable of navigating uncharted territories with remarkable precision and confidence.

Scroll to Top

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


Request for Video

    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


    Co-founder & CEO


    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.


    Request Free Demo

      RAMESH EMANI Board Member


      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.


      General Manager - Marketing

      A professional with a 14-year track record in technology marketing, Malavika heads marketing in Ignitarium. Responsible for all branding, positioning and promotional initiatives in the company, she has collaborated with technical and business teams to further strengthen Ignitarium's positioning as a key E R&D services player in the ecosystem.

      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.



      VP - Operations

      Pradeep comes with an overall experience of 26 years across IT services and Academia. In his previous role at Virtusa, he played the role of Delivery Leader for the Middle East geography. He has handled complex delivery projects including the transition of large engagements, account management, and setting up new delivery centers.

      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.

      In his role as the Head of Operations at Ignitarium, Pradeep leads and manages operational functions such as Resource Management, Procurement, Facilities, IT Infrastructure, and Program Management office.


      SONA MATHEW Director – Human Resources


      AVP – Human Resources

      Sona heads Human Resource functions - Employee Engagement, HR Operations and Learning & Development – at Ignitarium. Her expertise include deep and broad experience in strategic people initiatives, performance management, talent transformation, talent acquisition, people engagement & compliance in the Information Technology & Services industry.


      Prior to Ignitarium, Sona has had held diverse HR responsibilities at Litmus7, Cognizant and Wipro.


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



      Vice President - Sales

      As VP of Sales, Ashwin is responsible for Ignitarium’s go-to-market strategy, business, client relationships, and customer success in the Americas. He brings in over a couple of decades of experience, mainly in the product engineering space with customers from a wide spectrum of industries, especially in the Hi-Tech/semiconductor and telecom verticals.


      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.


      AZIF SALY Director – Sales


      Vice President – Sales & Business Development

      Azif is responsible for go-to-market strategy, business development and sales at Ignitarium. Azif has over 14 years of cross-functional experience in the semiconductor product & service spaces and has held senior positions in global client management, strategic account management and business development. An IIM-K alumnus, he has been associated with Wipro, Nokia and Sankalp in the past.


      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.


      RAJU KUNNATH Vice President – Enterprise & Mobility


      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.


      PRADEEP SUKUMARAN Vice President – Business Strategy & Marketing


      Vice President - Software Engineering

      Pradeep heads the Software Engineering division, with a charter to build and grow a world-beating delivery team. He is responsible for all the software functions, which includes embedded & automotive software, multimedia, and AI & Digital services

      At Ignitarium, he was previously part of the sales and marketing team with a special focus on generating a sales pipeline for Vision Intelligence products and services, working with worldwide field sales & partner ecosystems in the U.S  Europe, and APAC.

      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.