KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way (2209.15397v2)

Abstract: Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors. Our odometry estimation approach relies on point-to-point ICP combined with adaptive thresholding for correspondence matching, a robust kernel, a simple but widely applicable motion compensation approach, and a point cloud subsampling strategy. This yields a system with only a few parameters that in most cases do not even have to be tuned to a specific LiDAR sensor. Our system using the same parameters performs on par with state-of-the-art methods under various operating conditions using different platforms: automotive platforms, UAV-based operation, vehicles like segways, or handheld LiDARs. We do not require integrating IMU information and solely rely on 3D point cloud data obtained from a wide range of 3D LiDAR sensors, thus, enabling a broad spectrum of different applications and operating conditions. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.

• The paper challenges complex LiDAR odometry paradigms by showing that a minimal ICP approach can achieve competitive accuracy.
• It details a streamlined method using motion prediction, two-step voxel downsampling, and adaptive data association for precise registration.
• KISS-ICP's benchmark performance on KITTI and other datasets highlights its versatility across diverse platforms without extensive tuning.

Analysis of "KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way"

In this paper, the authors revisit classical point-to-point Iterative Closest Point (ICP) for LiDAR-based odometry and mapping, proposing a streamlined odometry system named KISS-ICP (Keep It Small and Simple). Their work challenges the necessity of overly complex solutions prevalent in much of modern LiDAR-based odometry, suggesting that simplicity, when executed correctly, can yield competitive results across various challenging conditions and sensor platforms.

Methodology

KISS-ICP focuses on a minimal yet effective approach for point cloud registration. Key elements include:

1. Motion Prediction and Compensation: The authors utilize a constant velocity model for predicting motion to deskew point clouds, compensating for motion distortion without the need for additional sensors like IMUs. This design choice simplifies the system and broadens its applicability across different platforms.
2. Point Cloud Subsampling: Instead of relying on complex feature extraction, the paper proposes spatial downsampling using a voxel grid, preserving original point cloud coordinates to avoid discretization errors. It uses a two-step downsampling process to optimize both mapping accuracy and computational efficiency.
3. Local Map Representation: A voxel grid stores the local map, and a hash table facilitates efficient memory usage and rapid nearest neighbor searches, aiding fast registration.
4. Adaptive Data Association: The paper introduces an adaptive threshold for correspondence matching based on observed robot accelerations, allowing the system to adjust to different motion profiles dynamically.
5. Robust Optimization: Registration is accomplished through a robust M-estimator that minimizes point-to-point residuals, with a termination criterion based solely on solution convergence rather than iteration count.

Results

On the KITTI odometry benchmark, KISS-ICP demonstrates performance competitive with state-of-the-art systems while maintaining simplicity. It ranks second among open-source methods on KITTI and effectively generalizes across additional datasets like MulRan, Newer College, and NCLT, regardless of the platform or motion profile—automotives, UAVs, segways, or handheld devices. In each case, KISS-ICP offers substantial accuracy without requiring dataset-specific parameter tuning, highlighting its robustness and versatility.

Discussion

The implementation details underscore the potential of revisiting classical methodologies with a focus on simplicity and adaptability. By stripping back to fundamental components and enhancing them with adaptive mechanisms, the authors illustrate that even established algorithms like point-to-point ICP can still be highly relevant and effective for modern applications, when executed with considered design choices.

The adaptation of the correspondence threshold based on actual data reflects a pragmatic approach to dealing with real-world variability, undercutting the need for manually fine-tuning parameters. Additionally, by abstaining from reliance on external sensors for motions compensation, the system remains flexible and widely applicable.

Implications and Future Work

The implications of this work suggest that future research in robotic perception and mapping could benefit from a more measured approach to complexity. As the KISS-ICP system operates faster than LiDAR frame rate under the tested conditions, real-time applications such as autonomous navigation may find it advantageous.

Future exploration could expand upon the adaptive threshold strategy, potentially integrating machine learning models to preemptively contextualize different environmental conditions based on prior experiences. Furthermore, exploring this system's integration with advanced loop closure techniques or collaborative mapping scenarios could yield greater insights into its broader practical utility.

Overall, the paper presents a compelling case for the continuing relevance of classical methods in sophisticated robotic applications, provided they are applied with an eye for practicality and flexibility. This work contributes not only a highly effective odometry solution but also encourages a re-evaluation of how complexity is approached in system design, particularly in the robotics AI community.