What is SLAM (Simultaneous Localization and Mapping)?

What is SLAM?

SLAM, which stands for Simultaneous Localization and Mapping, is a technique used in robotics and autonomous systems that solves two interrelated problems at once: figuring out where you are and building a map of your surroundings. Imagine being dropped into an unfamiliar building with no map and no GPS signal. As you walk around, you would simultaneously build a mental map of the building while keeping track of your position within it. SLAM enables robots and autonomous vehicles to do exactly this, but using sensors, algorithms, and computing power instead of human intuition.

The challenge is fundamentally circular: you need a map to know where you are, but you need to know where you are to build a map. SLAM algorithms solve this chicken-and-egg problem by iteratively refining both the map and the position estimate as new sensor data arrives, gradually building an increasingly accurate representation of both.

How SLAM Works

SLAM systems combine sensor data with sophisticated mathematical algorithms to solve the localization and mapping problem simultaneously:

Sensor Input

SLAM systems use one or more sensor types to perceive their environment:

• Lidar-based SLAM: Uses laser scanners to measure distances to surrounding objects, creating detailed 2D or 3D point cloud maps. Lidar SLAM is highly accurate and works well in both indoor and outdoor environments, making it the most common approach for industrial robotics and autonomous vehicles.
• Visual SLAM (vSLAM): Uses cameras to identify visual features in the environment. By tracking how these features move between frames, the system estimates its own motion and the 3D structure of the environment. Visual SLAM is cost-effective because cameras are inexpensive and lightweight.
• Radar SLAM: Uses radar sensors for mapping and localization, particularly useful in environments with poor visibility such as fog, rain, or dust.
• Multi-sensor SLAM: Combines multiple sensor types through sensor fusion to achieve more robust and accurate results than any single sensor approach.

Core Algorithm Steps

Regardless of the sensor type, SLAM algorithms follow a general process:

1. Observation: The system captures sensor data from its current position, identifying features or landmarks in the environment
2. Motion estimation: Using wheel odometry, inertial sensors, or visual motion estimation, the system predicts where it has moved since the last observation
3. Data association: The system matches currently observed features with previously mapped features to determine which landmarks it is seeing again
4. Map update: New observations are added to the map, and existing map features are refined based on new viewpoints
5. Loop closure: When the system recognises a previously visited location, it corrects accumulated errors in both the map and the position estimate. Loop closure is critical for maintaining map accuracy over extended operation.

Popular SLAM Approaches

• Extended Kalman Filter SLAM: One of the earliest approaches, suitable for small environments with limited landmarks
• Particle Filter SLAM (FastSLAM): Uses multiple hypotheses to handle uncertainty, suitable for larger environments
• Graph-based SLAM: Represents the SLAM problem as a graph optimisation problem, offering high accuracy and scalability. This is the most common approach in modern systems.
• Deep Learning SLAM: Uses neural networks to learn features and motion estimation directly from data, offering improved robustness in challenging environments

Business Applications

Warehouse and Logistics Robots

SLAM is the enabling technology for autonomous mobile robots (AMRs) that navigate warehouses, distribution centres, and factories without fixed infrastructure like rails or magnetic strips. Unlike older automated guided vehicles that follow predetermined paths, SLAM-equipped robots can navigate dynamically, adapt to layout changes, and operate in environments shared with human workers. This flexibility is driving rapid adoption in logistics operations across Southeast Asia.

Autonomous Vehicles

All autonomous vehicles use some form of SLAM for navigation, whether operating on public roads, in mines, on farms, or within ports. SLAM provides the real-time mapping and localization capability that enables vehicles to navigate safely in environments that may not have detailed pre-existing maps or where conditions change frequently.

Drone Operations

Drones use SLAM for autonomous navigation in GPS-denied environments such as indoor spaces, dense urban areas, and underground facilities. Applications include automated warehouse inventory using drones, infrastructure inspection inside buildings and tunnels, and agricultural monitoring under forest canopies.

Construction and Surveying

SLAM-equipped mobile scanners can rapidly create detailed 3D maps of construction sites, buildings, and infrastructure. This capability accelerates surveying, enables progress monitoring, and creates as-built documentation with significantly less time and cost than traditional surveying methods.

Facility Management

SLAM enables robots to autonomously map and navigate large facilities for tasks like cleaning, security patrols, and equipment inspection. The robot creates and continuously updates a map of the facility, adapting to changes like moved furniture or new obstacles.

SLAM in Southeast Asia

SLAM technology is finding growing adoption across several sectors in the region:

• Warehouse automation: As e-commerce grows rapidly across ASEAN, logistics companies in Singapore, Thailand, Malaysia, and Indonesia are deploying SLAM-equipped mobile robots to increase warehouse throughput and reduce fulfilment times.
• Manufacturing: SLAM-based autonomous mobile robots are transporting materials between workstations in factories across Thailand and Vietnam, replacing manual material handling and fixed conveyor systems with more flexible automation.
• Agriculture: SLAM enables autonomous navigation for agricultural robots and drones in the plantations and farms of Indonesia, Malaysia, and Thailand, where GPS signals can be unreliable under dense canopy cover.
• Construction: Singapore's construction industry, facing chronic labour shortages, is adopting SLAM-equipped robots and drones for site surveying, progress monitoring, and as-built documentation.

Common Misconceptions

"SLAM requires expensive lidar sensors." While lidar-based SLAM is the most established approach, visual SLAM using standard cameras has become increasingly capable and cost-effective. A basic visual SLAM system can operate on hardware costing a few hundred dollars, making it accessible for many applications.

"SLAM-equipped robots need no human supervision." While SLAM enables autonomous navigation, robots still need human oversight for task planning, exception handling, and safety monitoring. SLAM handles the "how to get there" part of autonomy, but humans typically manage the "what to do" and "when to do it" decisions.

"SLAM maps are always accurate." SLAM accuracy depends on sensor quality, environmental conditions, and algorithm tuning. Repetitive environments like uniform warehouse aisles, feature-poor spaces like empty rooms, and dynamic environments with many moving objects can challenge SLAM algorithms. Understanding these limitations is important for setting realistic deployment expectations.