VSLAM Blog 2 featured Photo
The second part of the Visual SLAM blog series discusses one of the key components in visual SLAM: the feature extraction pipeline, which includes triangulation and map point creation. These steps play a critical role in accurately estimating the robot's pose and creating a map of the surrounding environment.

VSLAM Series – Triangulation & Map Point Creation


The second part of the Visual SLAM blog series discusses one of the key components in visual SLAM: the feature extraction pipeline, which includes triangulation and map point creation. These steps play a critical role in accurately estimating the robot’s pose and creating a map of the surrounding environment. In this blog, we will delve into the fundamental concepts of triangulation and map point creation, their significance in visual SLAM, and various algorithms used to achieve robust localization and mapping.

For a better understanding of this topic, check out the first part of this series that provides a comprehensive study of different components of feature based visual SLAM algorithms, including their underlying principles.

1. Understanding Triangulation

Triangulation is a geometric process used in visual SLAM to estimate the 3D position of a feature point in the world based on its projections in two or more camera views. The underlying principle behind triangulation is to find the intersection of rays originating from the camera centers and passing through the image points. By computing the 3D position of these map points, the visual SLAM system can create a sparse 3D map of the environment.

1.1 Epipolar Geometry

Before diving into the details of triangulation, it is essential to grasp the concept of epipolar geometry. Epipolar geometry defines the relationship between corresponding points in two camera images and plays a crucial role in feature matching and triangulation. The epipolar geometry is defined by the epipolar line, which represents the possible positions of a corresponding point in the other image.

In a stereo vision system with two cameras, denoted as O_L and O_R, along with their respective image planes, a key property is observed: the camera centers, a 3D point, and its re-projections onto the images all lie in a common plane. When an image point is backprojected, it corresponds to a ray in 3D space. This ray, when projected back onto the second image, gives rise to a line known as the epipolar line (depicted in red in figure 1). Importantly, the 3D point lies on this ray, implying that the image of the 3D point in the second view must reside on the epipolar line.

To determine the relative pose between O_L and O_R, the essential matrix E comes into play. The essential matrix is an algebraic representation of epipolar geometry, specifically designed for situations where camera calibration is known. Utilizing the essential matrix, we can efficiently compute the relationship between the two camera centers and their relative orientation, providing valuable information for stereo vision and 3D reconstruction tasks.

Fig 1: Illustration of Epipolar Geometry [source]

1.2 Feature Matching

Feature matching is the process of finding corresponding features (also known as keypoints) between two or more camera images. Common feature detection algorithms like SIFT, SURF, ORB, and AKAZE are used to extract distinctive keypoints from the images. These keypoints are then matched using methods like brute-force matching, FLANN-based matching, or RANSAC to filter out incorrect matches and identify corresponding points.

2. Triangulation Methods

Several triangulation methods are used in visual SLAM to estimate the 3D positions of map points. Some of the popular ones include:

2.1 Direct Linear Transform (DLT) Triangulation

DLT is a straightforward triangulation method based on linear algebra. It involves solving a set of linear equations to find the 3D position of a map point from its 2D projections in multiple images. Although simple, DLT may suffer from numerical instability and is sensitive to noise and outliers.

Fig 2: Direct Linear Transformation [source]

Principle of DLT:

The DLT algorithm aims to establish a correspondence between points in the 3D world space and their projections onto the 2D image plane. It leverages a set of point correspondences between the 3D points and their 2D projections in multiple images taken from different camera poses. By solving a system of linear equations, DLT estimates the camera matrix, which encapsulates both the camera’s intrinsic parameters (focal length, principal point) and extrinsic parameters (rotation and translation).

2.2 Iterative Linear Least Squares Triangulation

This method improves upon DLT by using iterative optimization techniques like Gauss-Newton or Levenberg-Marquardt to refine the 3D position estimates. By iteratively minimizing the reprojection error (the difference between the actual 2D projections and the estimated 2D projections), this approach provides more accurate and robust 3D map points.

Principle of ILS:

ILS aims to refine the estimation of 3D landmark positions based on the observed 2D image points and corresponding camera poses. Given a set of camera poses and the associated image observations, the initial triangulation can be achieved using linear methods like the Direct Linear Transform (DLT). However, these linear methods may introduce inaccuracies due to noise, lens distortion, and other factors.

ILS leverages the least squares optimization framework to iteratively minimize the difference between the observed image projections of landmarks and their corresponding estimated projections based on the refined 3D positions. In each iteration, the algorithm adjusts the 3D positions of landmarks to minimize the error between observed and estimated projections. This process iteratively improves the triangulation results and provides more accurate 3D coordinates.

2.3 Non-linear Triangulation

Non-linear triangulation methods use non-linear optimization techniques to directly estimate the 3D position of map points by minimizing the reprojection error. These methods often yield better results than linear methods, especially in 

scenarios with significant noise or challenging lighting conditions.

Principle of Nonlinear Triangulation:

Nonlinear triangulation extends the concept of iterative optimization to address inaccuracies introduced by linear triangulation methods. It starts with an initial estimate of the 3D point positions based on observed 2D image points and corresponding camera poses. However, instead of treating the triangulation as a purely linear problem, nonlinear triangulation considers the impact of lens distortion, noise, and other non-linear effects.

The technique employs optimization algorithms, such as Levenberg-Marquardt or Gauss-Newton, to iteratively minimize the difference between the observed 2D projections of landmarks and their estimated projections based on the refined 3D positions. This iterative process refines the 3D coordinates of landmarks to more accurately align their projected positions with the observed image points.

3. Map Point Creation

Once the 3D positions of map points are estimated through triangulation, the visual SLAM system creates a map of the environment. The map points represent distinctive features in the scene that the robot can use for localization and navigation.

3.1 Map Representation

The map created by the visual SLAM system can be represented in different forms, such as a sparse or dense 3D point cloud, a keyframe-based map, or a feature-based map. Each representation has its advantages and is selected based on the specific application requirements and computational resources available.

Sparse 3D Point Cloud:

    • In a sparse 3D point cloud representation, the SLAM system maintains a collection of 3D points that represent key features or landmarks in the environment. These points are typically sparse, meaning that only a subset of distinctive and salient features from the scene is used for mapping.

    • Each 3D point is associated with a set of 2D image keypoints observed across multiple camera frames, allowing the SLAM system to estimate their 3D positions and camera poses simultaneously.

    • The sparse 3D point cloud is memory-efficient and useful for real-time applications, but it might not provide a complete representation of the environment.

Fig 3: Sparse 3D Point Cloud Map Represenation [source]

Keyframe-Based Map:

    • In a keyframe-based map, the SLAM system selects a subset of keyframes from the camera stream to represent the environment. Keyframes are frames with significant motion or high information content.

    • The keyframe-based map stores both camera poses and sparse or dense 3D points observed in those keyframes. These 3D points are triangulated from the feature matches between consecutive keyframes.

    • This representation offers a compact and efficient way to represent the map, as it focuses on important moments in the camera trajectory while still capturing relevant environmental features.

Fig 4: Keyframe Based Map Representation. Blue pyramids represent keyframe poses and coloured points are 3D landmarks.  [source]

Feature-Based Map:

    • A feature-based map represents the environment using distinctive visual features extracted from the camera images.

    • The map consists of a collection of features, such as keypoints, corners, or descriptors, along with their corresponding 3D positions. These features serve as landmarks for tracking and loop closure in the SLAM system.

    • Feature-based maps can be combined with either sparse or dense 3D point cloud representations to create a more comprehensive map.


Fig 5: Feature Based Map Representation. [source]

3.2 Feature Management

To maintain an efficient map, the visual SLAM system needs to manage the map points effectively. This includes adding new map points as the robot explores new areas, removing redundant or non-informative points to reduce memory usage, and optimizing the map structure to improve overall accuracy.

4. Challenges and Considerations

While triangulation and map point creation are essential steps in visual SLAM, they come with their own set of challenges:

4.1 Ambiguity and Occlusion

In certain scenarios, feature matching and triangulation can suffer from ambiguity due to repetitive patterns or lack of distinctive features. Occlusion, where parts of the scene are hidden from the camera view, can also affect the accuracy of feature matching and triangulation.

4.2 Dynamic Environments

In environments with moving objects, dynamic scenes, or changes in lighting conditions, maintaining a stable and consistent map becomes challenging. The SLAM system must handle dynamic map points and manage their presence in the map. Some key metrics are:

4.3 Real-time Performance

Visual SLAM systems often operate in real-time, requiring efficient algorithms for feature extraction, matching, triangulation, and map point management. Balancing accuracy and computational efficiency is a constant consideration in SLAM development.

  1. Frame Rate: The number of frames processed per second is a fundamental metric for real-time performance. It indicates how quickly the system can analyze incoming data, make computations, and provide updated information.
  2. Latency: This measures the delay between the sensor capturing data and the system producing a response. Low latency is critical in real-time applications to ensure the system reacts quickly to changes in the environment.
  3. Processing Time: It quantifies the time taken to perform specific computations, such as feature extraction, matching, triangulation, and map point management. Lower processing times indicate more efficient algorithms and implementations.
  4. CPU/GPU Utilization: Monitoring the usage of computational resources like CPU and GPU helps ensure the system is not overloaded. Efficient algorithms should utilize resources optimally without maxing out hardware capabilities.
  5. Memory Usage: Assessing how much memory the SLAM system consumes is crucial, especially for embedded or resource-constrained devices. Efficient algorithms use memory judiciously to store data and maintain functionality.
  6. Accuracy vs. Speed Trade-off: This metric balances the accuracy of the SLAM system against its speed. Systems often need to trade off accuracy to achieve real-time performance. Analyzing this trade-off helps developers choose the right compromise for their specific application.


Triangulation and map point creation are fundamental steps in visual SLAM that enable the creation of accurate 3D maps from camera images. Through triangulation, the system estimates the 3D positions of distinctive map points by identifying their projections in multiple images. These map points are then used to create a map of the environment, enabling the robot to localize itself and navigate its surroundings. Robust triangulation and map point creation are crucial for the success of visual SLAM systems, especially in challenging scenarios with dynamic environments and real-time requirements. As SLAM technology continues to advance, the development of efficient and accurate triangulation and map point creation methods remains a vital area of research and innovation.

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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.


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      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.


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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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      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.


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      SUJEET SREENIVASAN Vice President – Embedded


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


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      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.


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      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.


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      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.


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      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.