# Small Obstacle Avoidance Based on RGB-D Semantic Segmentation

arXiv: https://arxiv.org/abs/1908.11675 · HTML: https://mlsanity.com/paper/1908.11675/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [FSNet: A Failure Detection Framework for Semantic Segmentation](https://mlsanity.com/paper/2108.08748/) (2021)
- [Indoor Obstacle Discovery on Reflective Ground via Monocular Camera](https://mlsanity.com/paper/2401.01445/) (2024)
- [Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability](https://mlsanity.com/paper/2207.12939/) (2022)
- [Using a Distance Sensor to Detect Deviations in a Planar Surface](https://mlsanity.com/paper/2408.03838/) (2024)
- [SemGS: Feed-Forward Semantic 3D Gaussian Splatting from Sparse Views for Generalizable Scene Understanding](https://mlsanity.com/paper/2603.02548/) (2026)
- [Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones](https://mlsanity.com/paper/2202.01421/) (2022)
- [Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation](https://mlsanity.com/paper/2305.03259/) (2023)
- [A Waste Copper Granules Rating System Based on Machine Vision](https://mlsanity.com/paper/2207.04575/) (2022)
- [A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation](https://mlsanity.com/paper/2404.05858/) (2024)
- [Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D](https://mlsanity.com/paper/2603.05906/) (2026)
- [EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation](https://mlsanity.com/paper/2604.09694/) (2026)
- [PRISM: Multimodal Terrain Mapping for Rover Navigation in Unstructured Environments](https://mlsanity.com/paper/2607.16366/) (2026)

## Abstract (arXiv)

This paper presents a novel obstacle avoidance system for road robots equipped with RGB-D sensor that captures scenes of its way forward. The purpose of the system is to have road robots move around autonomously and constantly without any collision even with small obstacles, which are often missed by existing solutions. For each input RGB-D image, the system uses a new two-stage semantic segmentation network followed by the morphological processing to generate the accurate semantic map containing road and obstacles. Based on the map, the local path planning is applied to avoid possible collision. Additionally, optical flow supervision and motion blurring augmented training scheme is applied to improve temporal consistency between adjacent frames and overcome the disturbance caused by camera shake. Various experiments are conducted to show that the proposed architecture obtains high performance both in indoor and outdoor scenarios.