Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

TL;DR

This paper introduces a new benchmark for drivable area and road anomaly detection tailored for ground mobile robots, proposes a dynamic fusion module to improve feature integration, and demonstrates its effectiveness through extensive experiments.

Contribution

The paper develops a ground robot-specific benchmark, proposes the dynamic fusion module (DFM), and shows its superior performance over existing methods.

Findings

DFM-RTFNet outperforms state-of-the-art models.

Transformed disparity image is the most informative feature.

The proposed method achieves competitive results on KITTI benchmark.

Abstract

Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CNNs) have been proposed for pixel-wise drivable area and road anomaly detection. In addition, some benchmark datasets, such as KITTI and Cityscapes, have been widely used. However, the existing benchmarks are mostly designed for self-driving cars. There lacks a benchmark for ground mobile robots, such as robotic wheelchairs. Therefore, in this paper, we first build a drivable area and road anomaly detection benchmark for ground mobile robots, evaluating the existing state-of-the-art single-modal and data-fusion semantic segmentation CNNs using six modalities of visual features. Furthermore, we propose a novel module, referred to as the dynamic fusion module (DFM), which can be easily deployed in existing data-fusion networks to fuse different types of visual features effectively and efficiently. The experimental results show that the transformed disparity image is the most informative visual feature and the proposed DFM-RTFNet outperforms the state-of-the-arts. Additionally, our DFM-RTFNet achieves competitive performance on the KITTI road benchmark. Our benchmark is publicly available at https://sites.google.com/view/gmrb.