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.
翻译:语义同步本地化和绘图是一个对机器人和人工智能越来越感兴趣的主题,直接影响到自主汽车行业、军队行业等。这一领域的挑战之一是与机器人轨迹估计共同获得对象分类。考虑到依赖视景的语义测量,不同类别之间出现交错,从而产生一组数的假设。一个共同的解决方案是模拟概率足够低的假设,只保留数量有限的假设。然而,在调整和重新整顿后,更新后的概率对原概率过于自信。这对于需要高度精确的系统来说特别成问题。如果先前的班级概率是独立的,那么最初的正常化系数可以不经修饰的假设而有效计算。据我们所知,这是提出这些结果的第一个工作。如果这些班级的先前概率取决于足够低的概率,我们建议对确保谨慎结果的正常化系数有较低的约束。在调整和重新整顿后,约束是逐步计算的,其效率与独立案例相近。在调整和修改后,根据初始信念,这一信念将证明是接近的。