Object Instance Mapping in Semi-Dynamic Environments

Abstract

Object-level maps represent objects in an environment as a set of persistent instances; they support questions such as which objects are present, what they are, where they are, and how large they are. This thesis presents an online system that builds such a map for indoor mobile robots from RGB-D images and 2D LiDAR scans under imperfect detection, localization uncertainty, and changing object presence. Each object is represented by recursive beliefs over pose, physical extent, semantic class, and existence. Measurement uncertainty is calibrated empirically per class and expressed in a direction dependent, sensor-aligned frame. Observations are associated one-to-one by Mahalanobis distance for robustness to localization and measurement noise, and a viewpoint-novelty gate restricts geometric updates to sufficiently unique viewpoints, reducing overconfidence caused by treating repeated same-viewpoint observations as independent. Object existence is updated as an evidence-driven log-odds belief: detections provide positive evidence, while LiDAR free-space ray tracing provides negative evidence only when the predicted object location is confirmed empty. Evaluated on held-out recordings against motion-capture ground truth, the system maintains the correct object set under increasing synthetic Gaussian localization noise while achieving centimeter-level geometric accuracy and approximately consistent position covariances in the tested indoor setting.