What is SLAM Technology?
Simultaneous Localization and Mapping SLAM is a technology which allows a robot or autonomous systems to map an previously unknown environment and at the same time tracking its location in the map this is constructed. It would be comparable to searching a dark room with a flashlight, as you go, you shine and chart new territory and you keep tracing your location compared to what you already have discovered.
The underlying issue that SLAM addresses is that a robot must have a map to navigate itself, but it must know its location so that it can do the mapping. Such a chicken-and-egg dilemma is solved elegantly by SLAM algorithms in probabilistic methods, which allow keeping and updating beliefs of both the position of the robot and the map of its environment.
Why SLAM is Challenging?
Another issue of the SLAM is that implementing it is quite complicated because it has a large number of problems:
Sensor Noise
There exist in-built inaccuracies on all sensors that only increase with time and result into position inaccuracy.
Data Association
Mapping the current sensor readings to pre-existing features on the map is an inaccurate activity, especially in a repetitive environment.
Computational Load
When the size of the map increases, it becomes more complicated to keep and refurbish the robot position in regards to all features.
How SLAM Works
SLAM, in essence, is an amalgamation of sensor information and a probabilistic model to hold on to a belief regarding state of the robot and the environment. This can be explained in the following easy explanation:
Motion Prediction
Depending on the wheel encoder or IMU values the robot predicts its new location after motion.
Sensor Measurement
Features are identified in the environment relative to the robot with the help of sensors (LiDAR, camera, and so on).
Data Association
New measurements are compared with current features of the map or inserted as an additional landmark.
State Update
The robot odometry and map are updated aiming at minimising uncertainty.
Key SLAM Algorithms
Some of the algorithms that make the modern SLAM systems possible include:
Kalman Filter
A procedure, modeled in recursion, where a sequence of measurements with time is used to approximate variables.
Particle Filter
Represents the variety of possible states with many particles especially in systems which show no linearity.
Graph-Based SLAM
Represents the map which is a pose/constraints graph, this representation is optimized with least-square methods.
Kalman Filter Prediction Phase
Based on the system model predict the current state:
x̂ₖ⁻ = F x̂ₖ₋₁ + B uₖ
Predicted Covariance:
Pₖ⁻ = F Pₖ₋₁ Fᵀ + Q
Where:
- x̂ₖ⁻: Prior state estimate
- F: State transition matrix
- uₖ: Control input
- Pₖ⁻: Prior estimate covariance
- Q: Process noise covariance
Kalman Filter Update Phase
Put a value on the estimate with the measurements:
Kₖ = Pₖ⁻ Hᵀ (H Pₖ⁻ Hᵀ + R)⁻¹
Updated State:
x̂ₖ = x̂ₖ⁻ + Kₖ (zₖ – H x̂ₖ⁻)
Updated Covariance:
Pₖ = (I – Kₖ H) Pₖ⁻
Where:
- Kₖ: Kalman Gain
- R: Measurement noise covariance
- zₖ: Actual measurement
- H: Observation matrix
Real-World Applications
The technology of SLAM has led to the revolution in many sectors:
Autonomous Vehicles
SLAM allows self-driving cars to navigate the city streets and keep the maps always up to date.
Robotic Vacuums
Home robots develop navigational maps of your home to be able to go about cleaning it without collision.
Augmented Reality
AR devices apply SLAM to interpret physical places and place digital information on them.
Space Exploration
Rovers on planetary surfaces scan extraterrestrial landscapes, and pursue itineraries without GPS guidance.
Evolution of SLAM Technology
The development of SLAM has changed tremendously over the past thirty years:
Early Concepts
Initial probabilistic models and extended Kalman filter implementations.
Filter Improvements
The creation of the FastSLAM based on the use of particle filters and sparse information filters.
Graph Optimization
Optimization of pose graph and other efficient techniques of SLAM based on graphs such as g2o.
Deep Learning SLAM
Deep learning feature extraction, loop closure and sensor fusion.
The Future of Spatial Intelligence
SLAM technology is regularly improving at a fast rate, with new methods integrating deep learning, multi-sensors fusion, and more effective algorithms. With the continual maturity of these technologies, we will experience more competent autonomous systems to navigate and interact with our surroundings in ways we cannot conceive in science fiction until now.
The next frontier is include future directions of swarm SLAM (robots creating shared maps), long-term SLAM (maps that survive and get updated over a period of years), and semantic SLAM (understanding not only geometry, but semantics of things). This will power the second generation of autonomous technology to personal assistant-robots.