Extrinsic Calibration of Multiple LiDARs for a Mobile Robot based on Floor Plane And Object Segmentation

Core Concepts

Proposing a method for accurate extrinsic calibration of multiple LiDARs with non-overlapping FoV using floor plane and object point clouds.

Abstract

The content discusses the proposal of a target-less extrinsic calibration method for multiple LiDARs with non-overlapping field of view (FoV). The method utilizes accumulated point clouds of floor planes and objects while in motion to achieve accurate calibration. It addresses challenges posed by biased feature values and noise removal, demonstrating higher accuracy compared to conventional methods through simulations and real-world experiments. The approach involves rough refinement with planes and optimization with objects to create consistent 3D maps.
Structure:

Introduction to the importance of LiDAR in mobile robots.
Challenges in extrinsic calibration with multiple LiDARs.
Proposed method utilizing floor plane and object point clouds.
Evaluation through simulation and real-world experiments.
Results showing higher accuracy and successful 3D map creation.

Stats

"Our method pre-segment the floor plane and object point clouds, and perform a two-step estimation process that utilizes each point cloud."
"The proposed method can calibrate more accurately than conventional methods, regardless of object type."
"The proposed noise removal module outperforms traditional methods in noise reduction."

Quotes

"The proposed method achieves higher accuracy extrinsic calibration with two and four LiDARs than conventional methods, regardless type of objects."
"Our proposed noise removal module can eliminate noise more precisely than conventional methods."

Key Insights Distilled From

Extrinsic Calibration of Multiple LiDARs for a Mobile Robot based on Floor Plane And Object Segmentation

by Shun Niijima... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14161.pdf

Deeper Inquiries

How can this extrinsic calibration method be adapted for other sensor configurations or applications

This extrinsic calibration method can be adapted for other sensor configurations or applications by adjusting the segmentation and optimization processes to suit the specific characteristics of different sensors. For instance, if the sensors have overlapping fields of view, the segmentation step may need to account for this overlap to avoid redundant information. Additionally, the optimization process may need to consider different error metrics or constraints based on the sensor configuration. By customizing these steps according to the sensor setup, this calibration method can be effectively applied to various sensor configurations in robotics or autonomous systems.

What are potential limitations or drawbacks of relying on accumulated point clouds for calibration

One potential limitation of relying on accumulated point clouds for calibration is related to environmental variability. If there are significant changes in the environment between data collection sessions, such as moving objects or dynamic elements like people or vehicles, it could introduce inconsistencies in the accumulated point clouds. This variability might lead to inaccuracies in extrinsic calibration since the assumptions made during accumulation may no longer hold true.
Another drawback could be related to computational complexity and memory requirements when dealing with large volumes of accumulated point cloud data. Processing and storing extensive amounts of point cloud information from multiple LiDARs over time could strain computational resources and storage capacities.
Furthermore, relying solely on accumulated point clouds may not capture all possible scenarios that a robot encounters during operation. Certain features or objects that are crucial for accurate calibration might not appear frequently enough in the accumulated data, leading to suboptimal results.

How might advancements in lidar technology impact the effectiveness of this proposed calibration method

Advancements in lidar technology can significantly impact the effectiveness of this proposed calibration method by influencing both accuracy and efficiency aspects:

Increased Resolution: Higher resolution lidars would provide more detailed point cloud data, enabling finer feature extraction during segmentation processes. This enhanced resolution would improve object recognition and localization accuracy during extrinsic calibration.

Extended Range: Lidars with extended range capabilities would allow robots equipped with such sensors to gather more comprehensive environmental information over larger distances while moving. This expanded range could enhance motion estimation accuracy and contribute towards better overall system performance.

Improved Noise Reduction Techniques: Advanced noise reduction algorithms integrated into newer lidar models could streamline noise removal processes within this calibration method. Enhanced noise filtering capabilities would result in cleaner point cloud data inputs for more precise extrinsic parameter estimation.

4 .Faster Scanning Speeds: Lidars with faster scanning speeds would enable quicker acquisition of point cloud data during robot movement cycles, potentially reducing processing times for real-time extrinsic parameter calculations.