Robohub.org
 

A camera that knows exactly where it is


by
09 June 2021



share this:

Overview of the on-sensor mapping. The system moves around and as it does it builds a visual catalogue of what it observes. This is the map that is later used to know if it has been there before.
Image credit: University of Bristol

Knowing where you are on a map is one of the most useful pieces of information when navigating journeys. It allows you to plan where to go next and also tracks where you have been before. This is essential for smart devices from robot vacuum cleaners to delivery drones to wearable sensors keeping an eye on our health.

But one important obstacle is that systems that need to build or use maps are very complex and commonly rely on external signals like GPS that do not work indoors, or require a great deal of energy due to the large number of components involved.

Walterio Mayol-Cuevas, Professor in Robotics, Computer Vision and Mobile Systems at the University of Bristol’s Department of Computer Science, led the team that has been developing this new technology.

He said: “We often take for granted things like our impressive spatial abilities. Take bees or ants as an example. They have been shown to be able to use visual information to move around and achieve highly complex navigation, all without GPS or much energy consumption.

“In great part this is because their visual systems are extremely efficient and well-tuned to making and using maps, and robots can’t compete there yet.”

However, a new breed of sensor-processor devices that the team calls Pixel Processor Array (PPA), allow processing on-sensor. This means that as images are sensed, the device can decide what information to keep, what information to discard and only use what it needs for the task at hand.

An example of such PPA device is the SCAMP architecture that has been developed by the team’s colleagues at the University of Manchester by Piotr Dudek, Professor of Circuits and Systems from the University of Manchester and his team. This PPA has one small processor for every pixel which allows for massively parallel computation on the sensor itself.

The team at the University of Bristol has previously demonstrated how these new systems can recognise objects at thousands of frames per second but the new research shows how a sensor-processor device can make maps and use them, all at the time of image capture.

This work was part of the MSc dissertation of Hector Castillo-Elizalde, who did his MSc in Robotics at the University of Bristol. He was co-supervised by Yanan Liu who is also doing his PhD on the same topic and Dr Laurie Bose.

Hector Castillo-Elizalde and the team developed a mapping algorithm that runs all on-board the sensor-processor device.

The algorithm is deceptively simple: when a new image arrives, the algorithm decides if it is sufficiently different to what it has seen before. If it is, it will store some of its data, if not it will discard it.

Right: the system moves around the world, Left: A new image is seen and a decision is made to add it or not to the visual catalogue (top left), this is the pictorial map that can then be used to localise the system later. Image credit: University of Bristol

As the PPA device is moved around by for example a person or robot, it will collect a visual catalogue of views. This catalogue can then be used to match any new image when it is in the mode of localisation.

Importantly, no images go out of the PPA, only the key data that indicates where it is with respect to the visual catalogue. This makes the system more energy efficient and also helps with privacy.

During localisation the incoming image is compared to the visual catalogue (Descriptor database) and if a match is found, the system will tell where it is (Predicted node, small white rectangle at the top) relative to the catalogue. Note how the system is able to match images even if there are changes in illumination or objects like people moving.

The team believes that this type of artificial visual systems that are developed for visual processing, and not necessarily to record images, is a first step towards making more efficient smart systems that can use visual information to understand and move in the world. Tiny, energy efficient robots or smart glasses doing useful things for the planet and for people will need spatial understanding, which will come from being able to make and use maps.

The research has been partially funded by the Engineering and Physical Sciences Research Council (EPSRC), by a CONACYT scholarship to Hector Castillo-Elizalde and a CSC scholarship to Yanan Liu.

Paper



tags:


University of Bristol is one of the most popular and successful universities in the UK.
University of Bristol is one of the most popular and successful universities in the UK.





Related posts :



Robot Talk Episode 126 – Why are we building humanoid robots?

  20 Jun 2025
In this special live recording at Imperial College London, Claire chatted to Ben Russell, Maryam Banitalebi Dehkordi, and Petar Kormushev about humanoid robotics.

Gearing up for RoboCupJunior: Interview with Ana Patrícia Magalhães

and   18 Jun 2025
We hear from the organiser of RoboCupJunior 2025 and find out how the preparations are going for the event.

Robot Talk Episode 125 – Chatting with robots, with Gabriel Skantze

  13 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Gabriel Skantze from KTH Royal Institute of Technology about having natural face-to-face conversations with robots.

Preparing for kick-off at RoboCup2025: an interview with General Chair Marco Simões

and   12 Jun 2025
We caught up with Marco to find out what exciting events are in store at this year's RoboCup.

Interview with Amar Halilovic: Explainable AI for robotics

  10 Jun 2025
Find out about Amar's research investigating the generation of explanations for robot actions.

Robot Talk Episode 124 – Robots in the performing arts, with Amy LaViers

  06 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Amy LaViers from the Robotics, Automation, and Dance Lab about the creative relationship between humans and machines.

Robot Talk Episode 123 – Standardising robot programming, with Nick Thompson

  30 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Nick Thompson from BOW about software that makes robots easier to program.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence