You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.

Cited Abstract(s):
[1]: This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging LIDAR scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint e.g. puddles and snowdrifts, poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional 3D LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the z -height and reflectivity distribution of the environment-which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.
For self-driving vehicles many approaches have taken advantage of LIDAR reflectivity to achieve precise localization. These methods are commonly used in commercial approaches, for example, the approach of [1] uses a prior map of reflectivity and exploits road marks to reliably localize a vehicle in an urban environment. The authors formulate the world as a mixture of Gaussians (GMM) over 2D grid cells. The GMM represents the heights of points in each cell and the reflectance in a vigorous way that allows the approach to be robust to weather alteration and road degradation.