Simultaneous localization and mapping (SLAM) are essential in numerous robotics applications, such as autonomous navigation. Traditional SLAM approaches infer the metric state of the robot along with a metric map of the environment. While existing algorithms exhibit good results, they are still sensitive to measurement noise, sensor quality, and data association and are still computationally expensive. Alternatively, some navigation and mapping missions can be achieved using only qualitative geometric information, an approach known as qualitative spatial reasoning (QSR). We contribute a novel probabilistic qualitative localization and mapping approach in this work. We infer both the qualitative map and the qualitative state of the camera poses (localization). For the first time, we also incorporate qualitative probabilistic constraints between camera poses (motion model), improving computation time and performance. Furthermore, we take advantage of qualitative inference properties to achieve very fast approximated algorithms with good performance. In addition, we show how to propagate probabilistic information between nodes in the qualitative map, which improves estimation performance and enables inference of unseen map nodes - an important building block for qualitative active planning. We also conduct a study that shows how well we can estimate unseen nodes. Our method particularly appeals to scenarios with few salient landmarks and low-quality sensors. We evaluate our approach in simulation and on a real-world dataset and show its superior performance and low complexity compared to the state-of-the-art. Our analysis also indicates good prospects for using qualitative navigation and planning in real-world scenarios.

翻译：同时定位与地图构建（SLAM）在众多机器人应用中至关重要，例如自主导航。传统SLAM方法通过推断机器人的度量状态以及环境度量地图来实现功能。尽管现有算法效果良好，但它们仍对测量噪声、传感器质量及数据关联敏感，且计算成本高昂。另一种方案是仅利用定性几何信息完成部分导航与地图构建任务，这种方法被称为定性空间推理（QSR）。本文提出了一种新颖的概率性定性定位与地图构建方法。我们同时推断定性地图与相机姿态的定性状态（定位）。首次引入相机姿态之间的定性概率约束（运动模型），从而提升了计算效率与性能。此外，我们利用定性推理的特性实现了性能优越且极其快速的近似算法。进一步地，我们展示了如何在定性地图节点间传播概率信息，这既改进了估计性能，又能推断未观测地图节点——这是实现定性主动规划的重要基础模块。我们还通过实验评估了对未观测节点的估计效果。该方法尤其适用于标志物稀少且传感器质量较低的场景。我们在仿真环境与真实世界数据集上进行了评估，结果表明相较于现有技术，本方法具有更优的性能与更低的复杂度。相关分析也表明，定性导航与规划在真实场景中具有良好的应用前景。