论文标题
速度和多线估计的观察者级联
An observer cascade for velocity and multiple line estimation
论文作者
论文摘要
先前的增量估计方法考虑估计单线，需要与要映射的线路数量一样多。这导致需要至少拥有$ 4N $的状态变量，而$ n $是线路数。本文介绍了多行增量估计的第一种方法。由于线在结构化环境中很常见，因此我们旨在利用该结构以减少状态空间。本文提出的结构化环境的建模将状态空间降低至$ 3N + 3 $，并且也不太容易受到奇异配置的影响。以前方法的假设是始终可用摄像机速度。但是，速度通常是从嘈杂的探光仪中检测到的。考虑到这一点，我们建议将相机与惯性测量单元（IMU）和观察者级联耦合。第一个观察者检索线性速度的比例和线映射的第二个观察者。分析了整个系统的稳定性。该级联反应在渐近稳定上是渐进的，并显示在模拟数据的实验中收敛。
Previous incremental estimation methods consider estimating a single line, requiring as many observers as the number of lines to be mapped. This leads to the need for having at least $4N$ state variables, with $N$ being the number of lines. This paper presents the first approach for multi-line incremental estimation. Since lines are common in structured environments, we aim to exploit that structure to reduce the state space. The modeling of structured environments proposed in this paper reduces the state space to $3N + 3$ and is also less susceptible to singular configurations. An assumption the previous methods make is that the camera velocity is available at all times. However, the velocity is usually retrieved from odometry, which is noisy. With this in mind, we propose coupling the camera with an Inertial Measurement Unit (IMU) and an observer cascade. A first observer retrieves the scale of the linear velocity and a second observer for the lines mapping. The stability of the entire system is analyzed. The cascade is shown to be asymptotically stable and shown to converge in experiments with simulated data.
