论文标题
MRFMAP：使用向前射线传感器模型的在线概率3D映射
MRFMap: Online Probabilistic 3D Mapping using Forward Ray Sensor Models
论文作者
论文摘要
用于机器人映射的传统密集体积表示形式可以简化有关计算限制引起的传感器噪声特征的假设。我们提出了一个框架，该框架与传统的占用网格图不同，可以通过马尔可夫随机场明确对深度传感器的传感器射线形成进行建模，并执行循环信念传播，以推断地图中每个体素的边缘占用概率。通过明确推理闭塞，我们的方法模拟了地图中相邻体素之间的相关性。此外，通过合并学习的传感器噪声特性，我们即使使用传感器不确定性定义的嘈杂的传感器数据也可以执行准确的推断。我们提出了一个新的度量标准，用于评估概率体积图，并证明我们在模拟和现实世界数据集上的方法更高的保真度。
Traditional dense volumetric representations for robotic mapping make simplifying assumptions about sensor noise characteristics due to computational constraints. We present a framework that, unlike conventional occupancy grid maps, explicitly models the sensor ray formation for a depth sensor via a Markov Random Field and performs loopy belief propagation to infer the marginal probability of occupancy at each voxel in a map. By explicitly reasoning about occlusions our approach models the correlations between adjacent voxels in the map. Further, by incorporating learnt sensor noise characteristics we perform accurate inference even with noisy sensor data without ad-hoc definitions of sensor uncertainty. We propose a new metric for evaluating probabilistic volumetric maps and demonstrate the higher fidelity of our approach on simulated as well as real-world datasets.
