Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK.
Sensors (Basel). 2021 Dec 26;22(1):139. doi: 10.3390/s22010139.
OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap.
OctoMap 是一种高效的概率建图框架，可从点云中构建占据地图，使用八叉树中的立方节点表示 3D 环境。然而，OctoMap 的地图更新策略存在局限性。所有包含点的节点都将被分配相同的概率，而不管这些点是噪声，并且一个这样的节点的概率只能通过单次测量来增加。此外，内部有但被从传感器到端点投射的光线穿过的潜在占据节点将被标记为自由。为了克服 OctoMap 中的这些限制，目前的工作提出了一种使用相邻点上下文更新包含点的节点的映射方法，其中点的占据信息由点到其 k-最近邻居的平均距离表示。定义了距离与概率变化之间的关系，使用平均距离的累积密度函数，即使内部存在点，也有可能降低节点的概率。在 20 个数据集上进行了实验，将提出的方法与 OctoMap 进行了比较。结果表明，我们的方法可以比 OctoMap 的最佳性能提高高达 10%。
