NSF-BSF: RI: Small: Resource-Constrained Multi-hypothesis-aware Perception

NSF-BSF：RI：小型：资源受限的多假设感知感知

基本信息

批准号：
2008279
负责人：
Michael Kaess
金额：
$ 47.11万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008279&HistoricalAwards=false
关键词：
NSF BSF RI Small Resource

项目摘要

Mobile robots such as self-driving cars, service and household robots, will help people in their daily lives. They operate outside controlled factory environments where they need to perceive the world around them using onboard sensors such as cameras to self-localize, create maps, and perform tasks. Most current perception systems estimate the most likely state of the world but are poor at handling ambiguity and can therefore easily fail. In ambiguous situations, the most likely solution based on the currently available sensor measurements is selected, even though that might not be the one that corresponds to reality. Future measurements can disambiguate the situation, but if the correct solution has previously been discarded, it cannot be recovered, and the robot will fail in its task. This project focuses on developing novel algorithms that can deal with ambiguity for example by keeping track of multiple possible solutions. The key challenge is that the number of possible solutions can grow rapidly, and efficient solutions are needed that can be implemented with the restricted computational resources available onboard mobile robots.The novel methods to be investigated in this research will extend current state-of-the-art robust perception and belief space planning techniques by approximating the full set of potential hypotheses while simultaneously decreasing computational demands and providing probabilistic bounds on performance. Two different approximations will be investigated: (1) by grouping similar hypotheses into sets that can be approximately evaluated efficiently as a group and (2) by seeking to find a simplified set of hypotheses that is probabilistically close to the original unreduced set of hypotheses. After developing these methods in the passive case, they will be extended to the active perception situation where inherent tradeoffs will be investigated to attain online performance during both simulated and real-world experiments in environments where perceptual aliasing and ambiguity are prevalent.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自动驾驶汽车、服务机器人和家庭机器人等移动机器人将为人们的日常生活提供帮助。他们在受控工厂环境之外运行，需要使用相机等机载传感器来感知周围的世界，以进行自我定位、创建地图和执行任务。目前大多数感知系统都会估计世界最可能的状态，但在处理模糊性方面表现不佳，因此很容易失败。在模棱两可的情况下，会选择基于当前可用传感器测量结果的最可能的解决方案，即使这可能不是与现实相符的解决方案。未来的测量可以消除这种情况的歧义，但如果之前正确的解决方案已被丢弃，则无法恢复，机器人将无法完成任务。该项目专注于开发能够处理歧义的新颖算法，例如通过跟踪多种可能的解决方案。关键的挑战是可能的解决方案的数量可以快速增长，并且需要可以利用移动机器人上可用的有限计算资源来实现的有效解决方案。本研究中要研究的新颖方法将扩展当前的状态-艺术稳健的感知和信念空间规划技术，通过近似全套潜在假设，同时减少计算需求并提供性能的概率界限。将研究两种不同的近似：（1）通过将相似的假设分组为可以作为一个组进行有效近似评估的集合，以及（2）通过寻求在概率上接近原始未简化假设集的简化假设集。在被动情况下开发这些方法后，它们将扩展到主动感知情况，其中将研究固有的权衡，以便在感知混叠和模糊普遍的环境中的模拟和现实世界实验中获得在线性能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

InCOpt: Incremental Constrained Optimization using the Bayes Tree

InCOpt：使用贝叶斯树的增量约束优化

DOI：
10.1109/iros47612.2022.9982178
发表时间：
2022
期刊：
IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子：
0
作者：
Qadri, Mohamad;Sodhi, Paloma;Mangelson, Joshua G.;Dellaert, Frank;Kaess, Michael
通讯作者：
Kaess, Michael

Robust Incremental Smoothing and Mapping (riSAM)

鲁棒增量平滑和映射 (riSAM)

DOI：
10.1109/icra48891.2023.10161438
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
McGann, Daniel;Rogers, John G.;Kaess, Michael
通讯作者：
Kaess, Michael

ShapeMap 3-D: Efficient shape mapping through dense touch and vision

ShapeMap 3-D：通过密集的触摸和视觉进行高效的形状映射

DOI：
10.1109/icra46639.2022.9812040
发表时间：
2022
期刊：
IEEE International Conference on Robotics and Automation
影响因子：
0
作者：
Suresh, Sudharshan;Si, Zilin;Mangelson, Joshua G.;Yuan, Wenzhen;Kaess, Michael
通讯作者：
Kaess, Michael

ARAS: Ambiguity-aware Robust Active SLAM based on Multi-hypothesis State and Map Estimations

ARAS：基于多假设状态和地图估计的模糊感知鲁棒主动 SLAM

DOI：
10.1109/iros45743.2020.9341384
发表时间：
2020
期刊：
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
影响因子：
0
作者：
Hsiao, Ming;Mangelson, Joshua G.;Suresh, Sudharshan;Debrunner, Christian;Kaess, Michael
通讯作者：
Kaess, Michael

Tactile SLAM: Real-time inference of shape and pose from planar pushing