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.

研究通过精简核心元素实现了一个在各种环境条件下可操作的简单有效的传感器里程计估计系统，该系统在使用不同类型的 LiDAR 传感器时，仅具有少数参数并且不需要计算 IMU 信息，能够广泛应用于不同的应用场景。

KISS-ICP：支持点对点 ICP 的辩护 —— 如果正确实现，则简单、准确且健壮

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

The Iterative Closest Point (ICP) algorithm and its variants are a
fundamental technique for rigid registration between two point sets, with wide
applications in different areas from robotics to 3D reconstruction. The main
drawbacks for ICP are its slow convergence as well as its sensitivity to
outliers, missing data, and partial overlaps. Recent work such as Sparse ICP
achieves robustness via sparsity optimization at the cost of computational
speed. In this paper, we propose a new method for robust registration with fast
convergence. First, we show that the classical point-to-point ICP can be
treated as a majorization-minimization (MM) algorithm, and propose an Anderson
acceleration approach to speed up its convergence. In addition, we introduce a
robust error metric based on the Welsch's function, which is minimized
efficiently using the MM algorithm with Anderson acceleration. On challenging
datasets with noises and partial overlaps, we achieve similar or better
accuracy than Sparse ICP while being at least an order of magnitude faster.
Finally, we extend the robust formulation to point-to-plane ICP, and solve the
resulting problem using a similar Anderson-accelerated MM strategy. Our robust
ICP methods improve the registration accuracy on benchmark datasets while being
competitive in computational time.

本文提出了一种快速并具有鲁棒性的 Iterative Closest Point 方法，通过将点对点 ICP 视为主性 - 最小化算法，并采用 Anderson 加速方法加速其收敛速度并引入基于 Welsch 函数的鲁棒误差度量。在具有噪声和部分重叠的数据集上，我们实现了与稀疏 ICP 相似或更好的准确性，而速度至少快一个数量级。最后，我们将鲁棒的形式扩展到了点对平面 ICP，并使用类似的 Anderson 加速 MM 策略解决了相应的问题。