The emerging topic of cross-source point cloud (CSPC) registration has
attracted increasing attention with the fast development background of 3D
sensor technologies. Different from the conventional same-source point clouds
that focus on data from same kind of 3D sensor (e.g., Kinect), CSPCs come from
different kinds of 3D sensors (e.g., Kinect and { LiDAR}). CSPC registration
generalizes the requirement of data acquisition from same-source to different
sources, which leads to generalized applications and combines the advantages of
multiple sensors. In this paper, we provide a systematic review on CSPC
registration. We first present the characteristics of CSPC, and then summarize
the key challenges in this research area, followed by the corresponding
research progress consisting of the most recent and representative developments
on this topic. Finally, we discuss the important research directions in this
vibrant area and explain the role in several application fields.

本文系统回顾了 CSPC（Cross-source Point Cloud）配准的特点，关键挑战，研究进展和应用领域，并讨论了该领域的重要研究方向和多个传感器结合优势。

跨源点云配准：挑战、进展与前景

Cross-source Point Cloud Registration: Challenges, Progress and  Prospects

Dynamics modeling in outdoor and unstructured environments is difficult
because different elements in the environment interact with the robot in ways
that can be hard to predict. Leveraging multiple sensors to perceive maximal
information about the robot's environment is thus crucial when building a model
to perform predictions about the robot's dynamics with the goal of doing motion
planning. We design a model capable of long-horizon motion predictions,
leveraging vision, lidar and proprioception, which is robust to arbitrarily
missing modalities at test time. We demonstrate in simulation that our model is
able to leverage vision to predict traction changes. We then test our model
using a real-world challenging dataset of a robot navigating through a forest,
performing predictions in trajectories unseen during training. We try different
modality combinations at test time and show that, while our model performs best
when all modalities are present, it is still able to perform better than the
baseline even when receiving only raw vision input and no proprioception, as
well as when only receiving proprioception. Overall, our study demonstrates the
importance of leveraging multiple sensors when doing dynamics modeling in
outdoor conditions.

该篇论文研究如何在戶外和非结构化环境下使用多种传感器来构建预测机器人动态的模型，实验表明多种传感器的组合能够更准确地预测机器人的状态变化。

用于越野无人车的多模态动力学建模

Multimodal dynamics modeling for off-road autonomous vehicles

One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, denoising, resampling (align- ment) and exposure fusion as separate
problems. In contrast, in this paper we present a unifying approach, performing
HDR assembly directly from raw sensor data. Our framework includes a camera
noise model adapted to HDR video and an algorithm for spatially adaptive HDR
reconstruction based on fitting of local polynomial approximations to observed
sensor data. The method is easy to implement and allows reconstruction to an
arbitrary resolution and output mapping. We present an implementation in CUDA
and show real-time performance for an experimental 4 Mpixel multi-sensor HDR
video system. We further show that our algorithm has clear advantages over
existing methods, both in terms of flexibility and reconstruction quality.

该论文提出了一种基于多传感器的摄像机系统，旨在直接从原始传感器数据进行 HDR 组装的统一方法，其中包括适用于 HDR 视频的相机噪声模型和基于拟合局部多项式逼近观察到的传感器数据的空间自适应 HDR 重构算法。

多传感器 HDR 视频重建的统一框架

A Unified Framework for Multi-Sensor HDR Video Reconstruction

This is a preliminary version of visual interpretation integrating multiple
sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a
thorough integration of hierarchical Bayesian inference with comprehensive
physical representation of objects and their relations in a system for
reasoning with geometry, surface materials and sensor models in machine vision.
Bayesian inference provides a framework for accruing_ probabilities to rank
order hypotheses.

一篇关于基于贝叶斯推断以及多种传感器协同作用的机器视觉系统中，对物体和其属性进行全面分析和理解的研究论文。