Map completion from partial observation using the global structure of multiple environmental maps

Abstract

Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultaneous localization and mapping (SLAM) methods to understand the reachable area in newly visited environments. However, conventional mapping approaches are limited by only considering sensor observation and control signals to estimate the current environment map. This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. These map completion networks are primarily trained in the framework of generative adversarial networks (GANs) to extract the global structure of large amounts of existing map data. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation.

RSMPNet: Relationship Guided Semantic Map Prediction

“…Some researchers [12,13,20,23,27,29] have focused on predicting unobserved regions from partial observations to improve the robot's navigation performance. These works only predict an occupancy map using metric information to decide the subsequent goal.…”

“…GCN is widely used for capturing relational information. Some works [12,13,20,23,27,29] have used GCNs to extract semantic relationships from a pre-built object graph. In these works, a node usually denotes an object, while in our method, nodes are pixels, and edges are the semantic or spatial relationships among pixels.…”

“…To solve these problems, researchers [6,12,13,17,20,23,27,29] have started to study map prediction, which is also the aim of our work. As shown in Fig.…”

“…1, while the robot navigates in the environment, it takes semantic (obtained from some semantic segmentation algorithms) and depth images as input and then predicts the complete map from the accumulated partial map that it has seen. For the map prediction task, some previous works [12,13,20,23,27,29] model geometric information only, like occupancy and layout, while other works [6,17] infer semantics in unknown areas from the partial map to help the robot navigate to an object. However, these works usually make use of the information of object categories only without considering the relationships between them.…”