论文标题
混合信念修剪，并保证与观点相关的语义大满贯
Hybrid Belief Pruning with Guarantees for Viewpoint-Dependent Semantic SLAM
论文作者
论文摘要
语义同时定位和映射是对直接影响自动驾驶行业，陆军工业等的机器人技术和人工智能兴趣越来越多的主题。该领域的挑战之一是与机器人轨迹估计共同获得对象分类。考虑到依赖视图的语义测量值，不同类别之间存在一个耦合，从而导致了组合数量的假设。一个常见的解决方案是修剪具有足够概率且仅保留有限数量的假设的假设。但是，修剪和翻新后，更新的概率相对于原始概率过高。对于需要高精度的系统，这尤其有问题。如果类的先验概率是独立的，则可以在不修剪假设的情况下有效地计算原始归一化因子。据我们所知，这是第一项提出这些结果的工作。如果类的先验概率取决于，我们提出了确保谨慎效果的归一化因素的下限。界限是按逐步计算的，效率与独立情况相似。根据界限进行修剪和更新之后，这种信念在经验上被证明与原始信念接近。
Semantic simultaneous localization and mapping is a subject of increasing interest in robotics and AI that directly influences the autonomous vehicles industry, the army industries, and more. One of the challenges in this field is to obtain object classification jointly with robot trajectory estimation. Considering view-dependent semantic measurements, there is a coupling between different classes, resulting in a combinatorial number of hypotheses. A common solution is to prune hypotheses that have a sufficiently low probability and to retain only a limited number of hypotheses. However, after pruning and renormalization, the updated probability is overconfident with respect to the original probability. This is especially problematic for systems that require high accuracy. If the prior probability of the classes is independent, the original normalization factor can be computed efficiently without pruning hypotheses. To the best of our knowledge, this is the first work to present these results. If the prior probability of the classes is dependent, we propose a lower bound on the normalization factor that ensures cautious results. The bound is calculated incrementally and with similar efficiency as in the independent case. After pruning and updating based on the bound, this belief is shown empirically to be close to the original belief.
