Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance

• URL: http://arxiv.org/abs/2308.12845v1
• Date: Thu, 24 Aug 2023 15:10:28 GMT
• Title: Implicit Obstacle Map-driven Indoor Navigation Model for Robust Obstacle Avoidance
• Authors: Wei Xie, Haobo Jiang, Shuo Gu and Jin Xie
• Abstract summary: We propose a novel implicit obstacle map-driven indoor navigation framework for robust obstacle avoidance. A non-local target memory aggregation module is designed to leverage a non-local network to model the intrinsic relationship between the target semantic and the target orientation clues.
• Score: 16.57243997206754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust obstacle avoidance is one of the critical steps for successful goal-driven indoor navigation tasks.Due to the obstacle missing in the visual image and the possible missed detection issue, visual image-based obstacle avoidance techniques still suffer from unsatisfactory robustness. To mitigate it, in this paper, we propose a novel implicit obstacle map-driven indoor navigation framework for robust obstacle avoidance, where an implicit obstacle map is learned based on the historical trial-and-error experience rather than the visual image. In order to further improve the navigation efficiency, a non-local target memory aggregation module is designed to leverage a non-local network to model the intrinsic relationship between the target semantic and the target orientation clues during the navigation process so as to mine the most target-correlated object clues for the navigation decision. Extensive experimental results on AI2-Thor and RoboTHOR benchmarks verify the excellent obstacle avoidance and navigation efficiency of our proposed method. The core source code is available at https://github.com/xwaiyy123/object-navigation.

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