对象姿势预测的最新进展为机器人在导航期间构建对象级场景表示形式提供了有希望的途径。但是，当我们在新颖环境中部署机器人时，分发数据可能会降低预测性能。为了减轻域间隙，我们可以使用机器人捕获图像作为伪标签的预测在目标域中进行自我训练，以微调对象姿势估计器。不幸的是，姿势预测通常是折磨的，很难量化它们的不确定性，这可能会导致低质量的伪标记数据。为了解决这个问题，我们提出了一种猛烈支持的自我训练方法，利用机器人对3D场景几何形状的理解来增强对象姿势推断性能。将姿势预测与机器人探光仪相结合，我们制定并求解姿势图优化以完善对象姿势估计，并使伪标签在整个帧中更加一致。我们将姿势预测协方差纳入变量中，以自动建模其不确定性。这种自动协方差调整（ACT）过程可以在组件级别拟合6D姿势预测噪声，从而导致高质量的伪训练数据。我们在YCB视频数据集和实际机器人实验中使用深对象姿势估计器（DOPE）测试我们的方法。它在两种测试中的姿势预测中分别达到34.3％和17.8％的精度提高。我们的代码可在https://github.com/520xyxyzq/slam-super-6d上找到。

Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN 1 [21], proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.