# Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
## 2 Related Work  
Negative obstacle detection is a crucial aspect of autonomous navigation systems. [PERSON] et al. introduce a potential negative obstacle detection approach [2] based on occlusion labeling that extends traditional techniques by considering missing data interpretation and context-based labeling. This method involves 3D data accumulation, low-level classification, occluder propagation, and context-based labeling to identify potential negative obstacles. [PERSON] et al. present algorithms for off-road negative obstacle detection using LiDAR technology [3]. They utilize a point cloud generated by a 3D laser range finder and employ a geometry-based method called the Negative Obstacle Detector (NOD) along with a support vector machine (SVM) algorithm for classification. Despite advancements in obstacle avoidance and path planning, autonomous UGV systems still face challenges in accurately navigating off-road environments at high speeds. Various other studies have explored different approaches to negative obstacle detection. [PERSON] et al. [4] propose a novel 3D LiDAR setup and SVM-based method for effective negative obstacle detection in UGVs. [PERSON] et al. propose a detection algorithm [5] based on image sequences, while [PERSON] et al. introduce a feature fusion-based algorithm for LiDAR sensors [6]. [PERSON] and [PERSON] propose an obstacle detection algorithm for USVs that integrates IMU data with semantic segmentation, improving detection accuracy [7]. [PERSON] et al. [8] design a dual-LiDAR high-precision natural navigation system based on the ROS platform, utilizing Lidar-SLAM, the particle filter (PF) algorithm, and real-time motion correction to achieve accurate, unmanned navigation for automated warehousing and logistics. [PERSON] et al. [9] utilize imitation learning for reactive motion control in negative obstacle traversal with UGVs. [PERSON] et al. [10] propose a lightweight three-stage detection framework for railway obstacle detection, featuring a modified YOLOv4-tiny model (RODNet) that achieves competitive performance with lower power consumption. [PERSON] et al. propose a convex vision-based framework [11] for accurate and fast negative obstacle detection in autonomous vehicles,emphasizing the importance of depth estimations for real-time operations. These studies underscore the pivotal role of LiDAR in achieving precise negative obstacle detection, where high-resolution depth information is critical for reliable navigation. [PERSON] et al. [12] propose a dual-LiDAR method for autonomous land vehicles (ALVs) to detect both positive and negative obstacles, achieving high detection accuracy (98.5% for positive obstacles and 98.7% for negative obstacles) in various field environments. LiDAR technology has significant advantages in negative obstacle detection and can accurately provide the spatial location and morphological information of obstacles, but its limitation is that it cannot identify the specific types of obstacles and quantify their size, which limits its application in complex scenarios. In recent years, RGB-D cameras, as a sensor that combines RGB images and depth information, can provide richer environmental information and enhance obstacle classification and segmentation capabilities. Based on these research advances, RGB-D fusion, as an effective sensor data fusion method, has begun to show great potential in autonomous navigation and obstacle detection. By combining traditional visual information with depth data, RGB-D sensors can provide a more comprehensive and accurate perspective for environmental perception. [PERSON] et al. introduce a method for real-time high precision depth recovery by fusing intensity and depth information to create detailed range profiles without explicitly finding and integrating surface normals [13]. In the realm of scene recognition, [PERSON] et al. propose a discriminative multimodal feature fusion framework for RGB-D indoor scene recognition [14], considering inter- modality and intra-modality correlations to regularize learned features.