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

arXiv: https://arxiv.org/abs/2308.12845 · HTML: https://mlsanity.com/paper/2308.12845/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 43 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020)
- [PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning](https://mlsanity.com/paper/2201.10029/) (2022)
- [Visual Semantic Navigation using Scene Priors](https://mlsanity.com/paper/1810.06543/) (2018)
- [Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks](https://mlsanity.com/paper/1903.03878/) (2019)
- [ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation](https://mlsanity.com/paper/1905.10089/) (2019)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation](https://mlsanity.com/paper/1806.01054/) (2018)
- [Learning Object Relation Graph and Tentative Policy for Visual Navigation](https://mlsanity.com/paper/2007.11018/) (2020)
- [VTNet: Visual Transformer Network for Object Goal Navigation](https://mlsanity.com/paper/2105.09447/) (2021)
- [Bayesian Relational Memory for Semantic Visual Navigation](https://mlsanity.com/paper/1909.04306/) (2019)
- [SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation](https://mlsanity.com/paper/2012.04512/) (2020)
- [Visual Navigation with Spatial Attention](https://mlsanity.com/paper/2104.09807/) (2021)
- [Continuous Scene Representations for Embodied AI](https://mlsanity.com/paper/2203.17251/) (2022)
- [Hierarchical Object-to-Zone Graph for Object Navigation](https://mlsanity.com/paper/2109.02066/) (2021)
- [Stubborn: A Strong Baseline for Indoor Object Navigation](https://mlsanity.com/paper/2203.07359/) (2022)
- [Exploiting Scene-specific Features for Object Goal Navigation](https://mlsanity.com/paper/2008.09403/) (2020)
- [Object Memory Transformer for Object Goal Navigation](https://mlsanity.com/paper/2203.14708/) (2022)
- [Search for or Navigate to? Dual Adaptive Thinking for Object Navigation](https://mlsanity.com/paper/2208.00553/) (2022)
- [Unbiased Directed Object Attention Graph for Object Navigation](https://mlsanity.com/paper/2204.04421/) (2022)

## Built on by (most cited first, 3 of 3)

- [Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation](https://mlsanity.com/paper/2312.03327/) (2023)
- [Efficient Strategy Learning by Decoupling Searching and Pathfinding for Object Navigation](https://mlsanity.com/paper/2406.14103/) (2024)
- [GRIP: A Unified Framework for Grid-Based Relay and Co-Occurrence-Aware Planning in Dynamic Environments](https://mlsanity.com/paper/2510.10865/) (2025)

## Abstract (arXiv)

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.