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

• 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

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、服务和家用机器人等移动机器人将帮助人们的日常生活。他们在受控的工厂环境外工作，需要使用机载传感器(如摄像头)来感知周围的世界，以进行自我定位、创建地图和执行任务。大多数当前的感知系统都估计了世界上最有可能的状态，但在处理模棱两可方面做得很差，因此很容易失败。在模棱两可的情况下，选择基于当前可用的传感器测量的最有可能的解决方案，即使这可能不符合现实。未来的测量可以消除这种情况的歧义，但如果之前已经丢弃了正确的解决方案，它就无法恢复，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算法，例如，通过跟踪多个可能的解决方案。关键的挑战是可能的解的数量可以快速增长，并且需要有效的解可以在移动机器人上有限的计算资源下实现。本研究中要研究的新方法将通过逼近全部潜在假设集来扩展当前最先进的鲁棒感知和信念空间规划技术，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)通过寻求找到概率上接近原始的未约简假设集的简化的假设集。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，在这种情况下，将调查内在的权衡，以在感知混叠和模棱两可的环境中的模拟和真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势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

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
• 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
• 作者: McGann, Daniel;Rogers, John G.;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
• 作者: Hsiao, Ming;Mangelson, Joshua G.;Suresh, Sudharshan;Debrunner, Christian;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
• 作者: Suresh, Sudharshan;Si, Zilin;Mangelson, Joshua G.;Yuan, Wenzhen;Kaess, Michael
• 通讯作者: Kaess, Michael