Robust, Multi-sensor and Deployable Hybrid SLAM

稳健、多传感器和可部署的混合 SLAM

• 批准号: 566850-2021
• 负责人: Zelek, John
• 金额: $ 9.11万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=728734
• 关键词: Robust; Multi; sensor; Deployable; Hybrid

Visual odometry is the ability to localize with only a camera while moving. Visual SLAM (Simultaneous Localization And Mapping) extends this to also mapping the world simultaneously with only a single camera. The result is a mapping and localization method that produces LIDAR like maps with only an inexpensive camera. Direct methods use only pixel values while indirect methods use detected visual features. Hybrid methods leverage the advantages of Direct and Indirect approaches. We have developed a hybrid approach that leverages the advantages of each method which also overcomes the shortcomings of each approach in a computational efficient embodiment. Most research and commercial SLAM systems are Indirect methods while Direct methods are only present in research labs. Our approach provides Direct advantages such as sub-pixel accuracy, inexpensive computational costs, robustness under texture deprivation and provides both sparse & semi-dense reconstruction densities. Indirect advantages capitalized include robustness to large view distances, less sensitivity to optimization seeds and light change robustness. These combined advantages provide a global map query capability that supports map re-use. The method provides state-of-the-art low error performance in visual odomdetry, outperforming all existing hybrid, direct & indirect methods. In full SLAM mode after closure, our accumulated error is essentially zero. The objective of this project is to further develop the technology so that it is industry ready. This includes adding on-the-fly photometric and geometric calibration and adding the capability to tightly integrate other sensors such as LIDAR, other cameras, IMU, GPS to name a few. We also intend to improve the loop closure detection by developing a robust place detection and recognition system that may also be used to select keyframes. The goal is to have a system that can use more than one camera and is easily deployable and robust for various autonomous mapping applications with robots, vehicles, HD mapping and other applications.

视觉里程计是在移动时仅用相机定位的能力。 Visual SLAM（同时定位和映射）扩展了这一点，仅用一个摄像头就可以同时映射世界。 其结果是一种映射和定位方法，只需一个便宜的相机即可生成类似LIDAR的地图。直接方法仅使用像素值，而间接方法使用检测到的视觉特征。 混合方法利用了直接方法和间接方法的优势。 我们已经开发了一种混合方法，其利用了每种方法的优点，这也克服了计算高效实施例中每种方法的缺点。 大多数研究和商业SLAM系统是间接方法，而直接方法仅存在于研究实验室中。我们的方法提供了直接的优势，如亚像素精度，廉价的计算成本，纹理剥夺下的鲁棒性，并提供稀疏和半密集重建密度。 间接优势包括对大视角距离的鲁棒性、对优化种子的敏感性较低以及光变化鲁棒性。 这些组合的优点提供了支持地图重用的全局地图查询能力。 该方法在视觉odomdetry中提供了最先进的低错误性能，优于所有现有的混合，直接和间接方法。 在关闭后的完全SLAM模式下，我们的累积误差基本为零。 该项目的目标是进一步开发该技术，以便为工业做好准备。 这包括增加动态光度和几何校准，并增加紧密集成其他传感器的能力，例如LIDAR，其他相机，IMU，GPS等。 我们还打算通过开发一个强大的地方检测和识别系统，也可以用来选择关键帧，以提高循环闭合检测。 我们的目标是拥有一个可以使用多个摄像头的系统，并且对于机器人、车辆、高清地图和其他应用的各种自主地图应用来说，该系统易于部署和强大。