菜鸟看论文——Stochastic Occupancy Grids and Dynamic Programming（一）

最新推荐文章于 2024-05-25 17:16:43 发布

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最新推荐文章于 2024-05-25 17:16:43 发布 · 962 阅读

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本文探讨了概率占用网格和动态规划在复杂交通场景中计算自由空间的方法。概率占用网格通过维护每个单元格的占用概率，避免了确定性网格的离散化问题，但计算成本更高。动态编程用于极坐标网格中分割图像，实现全局优化，确保解决方案的空间和时间平滑性，同时保留空间和时间不连续性。该方法对于减少立体深度测量不确定性，特别是在远距离目标检测中的模糊问题，具有改进效果。

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参考论文：

07.Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming

08.Stereo-based Free Space Computation in Complex Traffic Scenarios

08.Dynamic Stereo Vision for Intersection Assistance

08.Towards optimal stereo analysis of image sequences

An occupancy grid M is a two-dimensional array or grid which models occupancy evidence of the environment

There are two main types of occupancy grids: deterministic and stochastic grids. Deterministic grids are basically bidimensional histograms counting 3D points. They are usually obtained by projecting the 3D view of measurements onto the road and counting the number of points falling onto the same cell area . The noise properties of the 3D measurements are not explicitly modeled. Grid cells with a large amount of hits are more likely to be occupied as those with few or no points. The choice of the discretization of cells of a determistic grid requires the usual compromise of any sampling process. Small discretization values might avoid the accumulation of points. Large discretization values loses spatial resolution, and therefore, estimation accuracy.

On the other hand, the cells of the stochastic occupancy grids maintain a likelihood or probability of occupancy. Instead of counting points, the stochastic grids define an update function which specifies the operation to perform on every cell, based on the measurement and its noise properties. Stochastic occupancy grids are more expensive to compute but are more informative and do not suffer from discretization effects, as compared to deterministic grids

However, the error of the triangulated 3D points is modelled as a Gaussian distribution. This leads to biased estimates of 3D position [11], and therefore to the wrong estimation of free space.

As mentioned in the introduction, it is common to accumulate all 3D points above ground in a stochastic occupancy grid. Figure 2 shows such a grid obtained for the urban situation considered in the sequel. The origin of the coordinate system is centered in our own vehicle. Our standard stereo camera system has a base line of 30cm and an angle of view of 42 deg. The imagers have VGA resolution.The Occupancy Grid Integration block performs a low-pass filter with the predicted occupancy grid based on the ego-motion of the camera. Finally, the free space is computed using dynamic programming on the polar grid.