Robust, Multi-sensor and Deployable Hybrid SLAM

点击翻译按钮获取中文摘要

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.