# Learning Autonomous Exploration and Mapping with Semantic Vision Source: [Learning Autonomous Exploration and Mapping with Semantic Vision]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:23:52.793Z Authors: Xiangyang Zhi, Xuming He, S\\"oren Schwertfeger Published: 2019-01-15T12:09:47.000Z ## Abstract We address the problem of autonomous exploration and mapping for a mobile robot using visual inputs. Exploration and mapping is a well-known and key problem in robotics, the goal of which is to enable a robot to explore a new environment autonomously and create a map for future usage. Different to classical methods, we propose a learning-based approach this work based on semantic interpretation of visual scenes. Our method is based on a deep network consisting of three modules: semantic segmentation network, mapping using camera geometry and exploration action network. All modules are differentiable, so the whole pipeline is trained end-to-end based on actor-critic framework. Our network makes action decision step by step and generates the free space map simultaneously. To our best knowledge, this is the first algorithm that formulate exploration and mapping into learning framework. We validate our approach in simulated real world environments and demonstrate performance gains over competitive baseline approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()