In this letter we investigate a tightly coupled Lidar-Inertia Odometry and Mapping (LIOM) scheme, with the capability to incorporate multiple lidars with complementary field of view (FOV). In essence, we devise a time-synchronized scheme to combine extracted features from separate lidars into a single pointcloud, which is then used to construct a local map and compute the feature-map matching (FMM) coefficients. These coefficients, along with the IMU preinteration observations, are then used to construct a factor graph that will be optimized to produce an estimate of the sliding window trajectory. We also propose a key frame-based map management strategy to marginalize certain poses and pointclouds in the sliding window to grow a global map, which is used to assemble the local map in the later stage. The use of multiple lidars with complementary FOV and the global map ensures that our estimate has low drift and can sustain good localization in situations where single lidar use gives poor result, or even fails to work. Multi-thread computation implementations are also adopted to fractionally cut down the computation time and ensure real-time performance. We demonstrate the efficacy of our system via a series of experiments on public datasets collected from an aerial vehicle.
翻译:在本信中,我们调查了一个紧密结合的Lidar-Inertia Odograph和映射(LIOM)计划,该计划能够纳入多个带互补视野的利达(LIOM)系统。实质上,我们设计了一个时间同步计划,将从独立的利达(Lidar)提取的特征整合成一个单一的点球,然后用来构建本地地图和计算地貌图匹配(FMM)系数(FMM)系数。这些系数与IMU预交观察一起,然后用来构建一个要素图,以优化生成滑动窗口轨迹的估计值。我们还提出了一个基于框架的关键地图管理战略,将滑动窗口中的某些成形块和点点块边缘化到边缘,以形成一个全球地图,然后用来在后一阶段将本地地图组装成。使用多个具有补充性FOV和全球地图的Lidars(FMMM)来确保我们的估计值的低漂移度并能够在单一里达尔使用导致或甚至无法工作的情况下维持良好的本地化。我们所收集的数据的计算结果也被采用。多读的计算方法,以便分分步地削减公共飞行器的计算过程。我们所收集的航空数据序列。