Cobots and Vision Pose estimation for pick and stow operations
Collaborative robots or cobots are being adopted in large fulfillment centers to streamline logistics with the intent to improve efficiency right from the stage of procurement to last-mile delivery. As the global collaborative robot market is forecasted to grow at a Compound Annual Growth Rate (CAGR) of 60% by 2030 [3], cobots are paving the way for collaborative, safe and productive warehouse automation.

Cobots and Vision: Pose estimation for pick and stow operations


Collaborative robots or cobots are being adopted in large fulfillment centers to streamline logistics with the intent to improve efficiency right from the stage of procurement to last-mile delivery.  

As the global collaborative robot market is forecasted to grow at a Compound Annual Growth Rate (CAGR) of 60% by 2030 [3], cobots are paving the way for collaborative, safe and productive warehouse automation. They are equipped with high degrees of autonomy, efficient navigation, and unrivaled flexible robotic manipulation. These robots can be made to work in conjunction with employees processing bulk orders with zero error in the shortest amount of time.  

Next Gen AMRs (Autonomous Mobile Robots) – Mobile Cobots 

Fig 1: Brief representation of a mobile cobot [4] with highlighted vital components 

Along with autonomy, navigation, and manipulation capabilities, it is becoming increasingly important to build Computer Vision capability in cobots. Vision helps cobots detect and locate objects, scan QR codes / bar codes and recognize patterns. In a traditional system, objects and obstacles should be presented to a cobot in a structured way. But with computer vision built into cobots, this is no longer necessary. This means the same cobots can handle several types of tasks – a cobot can be assigned to a particular set of tasks in the morning and a separate set in the afternoon. This provides a great degree of flexibility in operating cobots in warehouses and retail fulfilment centers.  

Computer Vision for Cobots 

Retail fulfilment centers deal with two variations of object picking application.  

Pick – Picking an object from the shelf and placing it in a bin 

Environmental conditions play a paramount role in the object pick task. The key factors which highly impact the vision module of the picking task are lighting variations on the shelf, width, and depth of the shelf stack area.  

Stow – Picking an object from the bin and stowing the same in the desired shelf. 

The sheer unstructured environment (where positions of other objects keep on changing every time an object is picked from the bin) leads to inevitable complexities and challenges in the vision module of object stow task.  

Along with the above, the challenges in camera placement, object localization and type of objects (variations in size, shape & reflection) are common to both tasks. 

To pick an object (irrespective of the tasks specified above), a robot requires the exact location of the object. This data is generated using a 2D depth map created by a stereo vision camera. Camera placement plays a critical role in the effective performance of Vision module and robotic manipulation. The generic factors to consider while choosing the right camera are the enterprise product depth/breadth, lighting, temperature, and other environmental conditions. 

Object localization brings in complexities in the case of the latter than the former, because of the involvement of cluttered scenes. The localization procedure involves identifying the location of the desired object in the scene, to facilitate object grasping by the robotic arm. 

The object location is calculated in terms of its position and orientation, also called pose estimation. The estimated pose is a critical input for robotic arm automation. 

In the below section, you will find a detailed description of the implementation of a pose estimation algorithm.  

Pose Estimation 

Open-Source Datasets 

The retail specific open-source datasets for 3D object pose estimation are not widely available. 

We use datasets like LINEMOD and YCB-Video throughout our experiments. These datasets deal with only a few retail objects. Also, we have captured an in-house dataset which includes 3 retail objects like a cereal box, coffee mug and soap box. This dataset capture is facilitated by Intel RealSense D435i camera and consists of images of individual objects and multiple objects in the scene. 

Dataset Collection – In-house dataset generation 

Pose estimation for every 3D object requires datasets comprising of both RGB and RGB-D images of objects with the corresponding ground truth values and transforms (rotation and translation). The common approach is to use multiple RGB-D sensors and high resolution DSLR cameras. However, such a setup requires a lot of resources and time. 

To generate effective ground truth values with low-cost camera set up, we followed an approach of using aruco marker tags. The aruco marker tags are attached to the retail objects and the corresponding rotation and translation matrices are calculated using OpenCV methods with aruco markers. 

Deep Learning Approach – Workflow and Results 

Pose estimation neural networks are widely categorized as pose regressor networks and 2D-3D correspondence networks. We use state-of-the-art pose estimation networks for our evaluation in different datasets. The basic workflow of our approach is as shown in fig 2. 

Fig 2: Workflow of our approach 

On LINEMOD dataset, for symmetric object (Egg box) and an asymmetric object (Driller), the measured Average Distance Difference (ADD) and translation errors are 0.6235 and 26.01 mm for the former and 0.75 and 17.04 mm for the latter. Below are a few of our visualization results evaluated on our implemented model for LINEMOD dataset.

Fig 3: Predicted 3D bounding box(blue) and ground truth(green) for Egg box (Image on left) and Driller (Image on right) on LINEMOD dataset 

As proof of the generalizability of our deep learning models, we also present the visualization results on our in-house dataset. 

Fig 4: Predicted 3D bounding box (in green) for the objects in our in-house dataset 

With the ever-increasing SKU (stock keeping units) range, real-time dataset collection procedure has become extremely complicated.  

Owing to the data-centric AI approach, high quality data can be artificially generated and labelled. This synthetic data can be used for further training and testing of the model, leading to better overall performance results. 

Accelerated mobile cobot deployment using digital twin and synthetic data 

Any kind of simulation; whether it is data or a physical entity (fulfillment center floor, in our case) is an inexpensive alternative to real-world procedures. On one hand, synthetic data is the simulation of real-world data for AI models. While on the other hand, digital twin is a cost-effective simulation of physical space, people, and processes. Synthetic data when used in conjunction with digital twin effectively accelerates the validation for robotic systems and thus, eventually leads to predictive maintenance.  

Here we briefly shed light on some of the current market-disrupting platforms (listed below) which help in resolving cross-domain (synthetic and real-time) problems. 

Fig 4: A labeled synthetic data sample, sourced from Unity3D [6] 

AWS (Amazon Web Services) Platform and 3D Excite 

Dassault Systems 3D Excite in combination with Amazon SageMaker, render 3D images of objects and annotate the data, respectively. Thus, a newly generated dataset can be effectively stored in Amazon Simple Storage Service (Amazon S3). Finally, Amazon Rekognition Custom labels imports the training dataset from storage and facilitates testing of the model with real-time images [1].


Effective synthetic dataset generation involves automatic scene generation with variations of lighting, object hue, blur, and noise. Unity Perception package helps in handling the placement & orientation of the object, desired scene generation with different variations and background environment [2]. 


As cobots are being touted as the next wave in warehouse automation, the need for computer vision in mobile cobots is a necessary technology for automating tasks like pick and stow, packaging and many others in warehouses which (otherwise) would have simply not been possible.  

With the advent of mobile cobots  in retail fulfillment centers, there is increased traction for expertise in Robotics in collaboration with AI (Artificial Intelligence).  

With our expertise in 3D computer vision, mobile cobot navigation and manipulation, and synthetic dataset generation, we continue to work alongside robotics companies to adapt cobots for different use cases.





[4] Manipulator Image source: 



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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


    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.


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      Board Member

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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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      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.


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      Chief Technology Officer


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      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.


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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".


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