AI is critical for empowering robots and drones with autonomous capabilities like navigating environments and recognising objects. In this article, we take a deeper look at how innovative AI models allow machines to map spaces, localise themselves, and interpret visual data.

SLAM: Building Maps and Localising Within Them

A core aim of robotics AI is to perform simultaneous localisation and mapping (SLAM). This involves incrementally constructing a map of an unknown environment while also tracking the robot’s location within it. This enables autonomous navigation even in spaces the robot has never previously encountered.

SLAM algorithms leverage sensors like lidar, sonar, and cameras to scan and collect data during motion. As the robot moves, the AI aggregates sensor readings into an expanding map of walls, objects, and other surfaces. This map gradually becomes a spatial reference model.

Concurrently, the AI compares real-time scans against the updated map to estimate the robot’s pose - its location and orientation. By continuously matching sensor data with the evolving map, the robot can track its coordinates as it explores. The algorithms also identify errors between scans and predictions to correct pose estimates over time.

Under the bonnet, SLAM AI implements probabilistic filtering algorithms like Kalman filters and particle filters. These model the uncertainty in sensor readings and predictions to maintain optimal location estimates as the robot moves. The algorithms also build graphs linking poses, map points, and observations.

The output is a coherent spatial map the robot can leverage for path planning and goal-directed navigation without needing manual guidance. Even if the initial location is unknown, the constant influx of sensor data enables robust pose estimation and mapping.

Convolutional Neural Networks for Object Recognition

For autonomous robots, a crucial complement to spatial mapping is recognising objects and obstacles in the environment. This visual perception is enabled by AI architectures like convolutional neural networks (CNNs).

CNNs are trained on large, labelled datasets of images containing objects of interest like cars, signs, animals, or food items. By processing thousands to millions of sample images, CNNs learn to recognise patterns in pixel data associated with each object category.

CNNs consist of layered neural networks that filter and condense visual data into high-level abstractions. Lower layers detect basic edges and textures. Higher layers assemble these into complex objects through repeated pooling, filtering, and classifications.