Robotic practitioners generally approach the vision-based SLAM problem through discrete-time formulations. This has the advantage of a consolidated theory and very good understanding of success and failure cases. However, discrete-time SLAM needs tailored algorithms and simplifying assumptions when high-rate and/or asynchronous measurements, coming from different sensors, are present in the estimation process. Conversely, continuous-time SLAM, often overlooked by practitioners, does not suffer from these limitations. Indeed, it allows integrating new sensor data asynchronously without adding a new optimization variable for each new measurement. In this way, the integration of asynchronous or continuous high-rate streams of sensor data does not require tailored and highly-engineered algorithms, enabling the fusion of multiple sensor modalities in an intuitive fashion. On the down side, continuous time introduces a prior that could worsen the trajectory estimates in some unfavorable situations. In this work, we aim at systematically comparing the advantages and limitations of the two formulations in vision-based SLAM. To do so, we perform an extensive experimental analysis, varying robot type, speed of motion, and sensor modalities. Our experimental analysis suggests that, independently of the trajectory type, continuous-time SLAM is superior to its discrete counterpart whenever the sensors are not time-synchronized. In the context of this work, we developed, and open source, a modular and efficient software architecture containing state-of-the-art algorithms to solve the SLAM problem in discrete and continuous time.
翻译:机械化实践者通常通过离散时间的配方处理基于愿景的 SLAM 问题,这具有综合理论的优势,并且很好地理解成功和失败案例。然而,离散时间的SLAM 需要量身定制的算法,并且当来自不同传感器的高率和/或非同步的测量在估算过程中出现时,需要简化假设。相反,连续时间的SLAM 往往被执业者忽视,并不受到这些限制。事实上,它允许不同步地将新的传感器数据纳入基于愿景的SLM 问题,而不会为每一项新的计量增加新的优化变量。这样,整合传感器数据的不同步或连续的高比率流并不需要量身定制和高度设计的算法,从而能够以直观的方式将多种传感器模式融合起来。另一方面,连续的时间引入之前可能会使一些不易变的模块中的轨迹估计更加恶化。我们的目标是系统地比较基于愿景的SLMM 的两种配方的优点和局限性。为此,我们进行了广泛的实验性分析,不同的机器人类型、运动速度和感官级流,无论何时,我们不断的SLSLM 型和感官型的直径式的系统分析都显示不断的系统化的系统化的系统结构。