What is Loop Closure Detection? Understanding Its Role in SLAM

As a robot continues mapping over extended paths, cumulative errors inevitably arise. While local and global optimization methods can help reduce these errors, loop closure detection provides another powerful mechanism to correct pose estimation and improve mapping accuracy.

What is Loop Closure Detection?

Loop closure detection—also known as loop detection—is the ability of a robot to recognize that it has returned to a previously visited location, thereby “closing the loop” in its map.

Loop Detection Success

In simple terms, when a robot turns left and right while mapping, loop closure enables it to realize: “I’ve been here before”, and then align the newly generated map segment with the previously created one.

Successful loop closure: greatly reduces cumulative errors, improving navigation and obstacle avoidance.
Failed loop closure: may corrupt the entire map.

This is why loop closure is considered critical for building large-scale and complex maps.

Loop Detection Failed

How to Improve a Robot’s Loop Closure Capabilities

1.Graph-Based SLAM Algorithms

Graph-optimized SLAM 3.0 represents a major breakthrough in loop closure detection.

SLAM 3.0 integrates image data into a topological graph.
When the robot re-enters a known environment, it actively detects loops using the internal graph structure.
Upon detecting a loop, it applies optimization algorithms such as Bundle Adjustment (BA) to refine the pose graph and correct the map.

SLAM 3.0 Loop Closure Test

Key logic:

Detect small loops first, then larger loops.
Select feature-rich points as loop closure anchors.
Traverse overlapping paths to refine loop details.

Even in large-scale scenarios, SLAM 3.0 can maintain reliable mapping.

Complete closure process of mapping in ultra-large scenes

2.Bag-of-Words (BoW) Model

In addition to upgrades and optimizations of SLAM algorithms, many systems now adopt the well-established Bag-of-Words (BoW) model to assist robots in performing loop closure. Simply put, this approach matches features between frames.

From each image, feature points and descriptors are extracted. Since a descriptor is typically a multi-dimensional vector, the distance between two descriptors can be computed. These descriptors are then clustered (e.g., using k-means), where the number of clusters corresponds to the vocabulary size—for example, 1,000 words. Other methods such as Bayes or SVM can also be used.

K-ary tree dictionary

Finally, the vocabulary is organized into a tree structure to facilitate efficient searching.

3.Similarity Computation

This approach determines loop closure relationships based on the visual similarity between two images. But how can we establish the correlation between two maps?

For example, given image A and image B, we want to compute their similarity score s(A,B). A straightforward way would be to subtract the two images and take the norm:

s(A,B)=||A−B||s(A,B)=||A−B||

However, this method is highly sensitive to differences in viewpoint and lighting conditions, making it unreliable. Instead, a more robust similarity computation formula is applied.

Here, we introduce one such method: TF-IDF (Term Frequency–Inverse Document Frequency).

TF (Term Frequency): features occurring frequently in one image gain importance.

IDF (Inverse Document Frequency): rare features across the dictionary carry higher discriminative power.

For the IDF component, assume the total number of features is n, and the number of features contained in a specific node Wi is Ni. Then, the IDF of that term is defined as:

TF refers to the frequency of a specific feature within a single image. Assume that in image A, the term Wi appears N times, and the total number of terms in the image is n. Then the TF is defined as:

Thus, the weight of Wi is given by the product of TF and IDF, i.e.:

After applying the weights, we can obtain a set of terms for a given image, resulting in a Bag-of-Words (BoW) representation:

To calculate the similarity between two images, the L1 norm is used in this case:

4.Deep Learning and Other Approaches

Loop closure can also be treated as a pattern recognition task, using machine learning methods such as decision trees, SVMs, or deep learning models to identify revisited places.

Final Note

When a loop is detected, the robot should not immediately stop. Instead, it should continue moving along overlapping paths to refine and improve map details, ensuring a more accurate and consistent representation of the environment.Continue along overlapping paths to refine loop closure details.

Keywords: SLAM,Technology Explained

이전 뉴스

다음 뉴스

Related news

Comparison of Laser SLAM and Visual SLAM: Advantages and Disadvantages

LiDAR Channels: Differences Between Single-Line and Multi-Line LiDAR

What is Loop Closure Detection? Understanding Its Role in SLAM

Detailed Explanation of the Laser Triangulation Ranging Principle

How Much Do You Know About Laser SLAM Mapping Principles and Tips?

Lidar vs. Millimeter-Wave Radar: What’s the Difference?

What is SLAM? A Beginner’s Guide from Zero to One

What is Loop Closure Detection? Understanding Its Role in SLAM