New in the world of SLAM

We build spatial intelligence for robots. Right now that means a SLAM based drone, built and tested in simulation first, so it can map, plan and fly where GPS cannot reach.

Read the research Meet Veg

What we work on

Four areas, one goal: a robot that knows where it is and decides well what to do next.

SLAM and path planning

Visual and inertial mapping that keeps a robot localized indoors, underground and anywhere satellites drop out, with planners that turn the map into a safe route.

Deep learning

Perception that runs on the robot itself: detection and recognition today, and learned 3D models that make maps denser and tracking more reliable.

Reinforcement learning

Robots that learn decisions from experience, built on the math of Markov decision processes.

Robotics and robot learning

Robots that learn skills by watching people, and the hardware and control that let them act on what they learn.

Veg, our drone that flies without GPS

Veg maps its surroundings with visual inertial SLAM, plans its own route, recognises people and objects onboard and lands safely after a rotor failure. The research version runs entirely on a Raspberry Pi 4 and an Arduino Nano, and is described in a paper on arXiv.

Localization: ORB-SLAM3, monocular and inertial
Planning: Dijkstra over a map built from SLAM points
Control: LQR attitude loop, PD position loop
Safety: Rotor fault detection with rerouted landing
Vision: Onboard object detection and face recognition

Now: building Veg 3.0 in simulation

Read about Veg Paper on arXiv

Veg quadcopter layout with Raspberry Pi 4, IMU, ESC and Pi camera labelled — Veg hardware layout: Raspberry Pi 4, IMU, ESCs and Pi camera.

Maze map showing the original mission path, the rerouted path after a rotor fault, and the fault location — After a simulated rotor fault, Veg reroutes to the nearest safe landing zone.

Latest research

SLAM 21 Oct 2026 11 min read

A* Search Algorithm Explained: f = g + h, Heuristics and Python Code

A* search explained: how f = g + h works, admissible heuristics, weighted A*, a tested Python example and a TurtleBot3 running A* in ROS 2.
SLAM 21 Oct 2026 9 min read

Dijkstra's Algorithm Explained: Cheapest Paths for Robots, with Python Code

Dijkstra's algorithm explained step by step: priority queues, edge relaxation, why it finds the cheapest path, and a tested Python example on a robot grid map.
SLAM 21 Oct 2026 9 min read

Breadth First Search (BFS) Explained: Shortest Paths for Robots, with Python Code

Breadth first search explained from the ground up: how the queue works, why BFS finds the fewest steps path, and a tested Python example on a robot grid map.
SLAM 21 Oct 2026 4 min read

Visual SLAM in 2026: Foundation Models Now Map in Real Time

The biggest visual SLAM updates of 2026: VGGT-SLAM 2.0, AIM-SLAM, LeanGate and the Hilti Trimble challenge, and what they mean for drones that fly without GPS.
Reinforcement learning 16 Oct 2026 14 min read

Markov Decision Process (MDP) Explained: Theory, Math and Python Code

Learn Markov decision processes step by step: the Markov property, Bellman equations, value iteration and policy iteration, with a full Python grid world example.