LiDAR Point Research Articles

Boundary points guided 3D object detection for point clouds

3D object detection in LiDAR point clouds is crucial for computer vision tasks such as autonomous driving. The two-stage approach with point cloud completion achieves remarkable performance by generating semantic surface points for foreground objects or learning bird’s eye view shape heat map labels. However, these methods require additional completion datasets, leading to substantial computation and memory demands. In this context, we propose a Boundary Points Guided 3D (BPG3D) object detection method that complements point cloud boundary information without the need for additional data. Specifically, we generate Region of Interest (RoI) boundary points to aggregate the neighbor voxel information at the RoI boundary during the refinement stage to complement the missing boundary information. Meanwhile, we design a Dual Feature Selection (DFS) module to adaptively fuse RoI grid point features and RoI boundary point features for bounding box refinement with negligible computational cost. Additionally, inspired by tensor decomposition theory, we use low-rank tensors to reconstruct high-rank tensors in the point cloud feature encoder to enhance contextual semantic information. The proposed method achieves 65.81% mAP on KITTI Test Set, obtaining a good trade-off between accuracy and efficiency.

Synthetic lidar point cloud generation using deep generative models for improved driving scene object recognition

The imbalanced distribution of different object categories poses a challenge for training accurate object recognition models in driving scenes. Supervised machine learning models trained on imbalanced data are biased and easily overfit the majority classes, such as vehicles and pedestrians, which appear more frequently in driving scenes. We propose a novel data augmentation approach for object recognition in lidar point cloud of driving scenes, which leverages probabilistic generative models to produce synthetic point clouds for the minority classes and complement the original imbalanced dataset. We evaluate five generative models based on different statistical principles, including Gaussian mixture model, variational autoencoder, generative adversarial network, adversarial autoencoder and the diffusion model. Experiments with a real-world autonomous driving dataset show that the synthetic point clouds generated for the minority classes by the Latent Generative Adversarial Network result in significant improvement of object recognition performance for both minority and majority classes. The codes are available at https://github.com/AAAALEX-XIANG/Synthetic-Lidar-Generation.

An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids

An efficient ground segmentation approach for LiDAR point cloud utilizing adjacent grids

Efficient Path Planning Algorithm Based on Laser SLAM and an Optimized Visibility Graph for Robots

Mobile robots’ efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms.

MS23D: A 3D object detection method using multi-scale semantic feature points to construct 3D feature layer

LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. Many studies accomplish the 3D object detection by voxelizing point clouds. However, in autonomous driving scenarios, the sparsity and hollowness of point clouds create some difficulties for voxel-based methods. The sparsity of point clouds makes it challenging to describe the geometric features of objects. The hollowness of point clouds poses difficulties for the aggregation of 3D features. We propose a two-stage 3D object detection framework, called MS23D. (1) We propose a method using voxel feature points from multi-branch to construct the 3D feature layer. Using voxel feature points from different branches, we construct a relatively compact 3D feature layer with rich semantic features. Additionally, we propose a distance-weighted sampling method, reducing the loss of foreground points caused by downsampling and allowing the 3D feature layer to retain more foreground points. (2) In response to the hollowness of point clouds, we predict the offsets between deep-level feature points and the object’s centroid, making them as close as possible to the object’s centroid. This enables the aggregation of these feature points with abundant semantic features. For feature points from shallow-level, we retain them on the object’s surface to describe the geometric features of the object. To validate our approach, we evaluated its effectiveness on both the KITTI and ONCE datasets.

Evaluating the applicability of high-density UAV LiDAR data for monitoring tundra grassland vegetation

ABSTRACT Grasslands are one of the most widespread biomes on Earth. The interaction of UAV LiDAR with grasses and herbaceous plants is an infrequently covered area of research, apart from its utilisation for the estimation of above ground biomass. To evaluate the applicability of LiDAR for monitoring grassland vegetation, two model plots in the arctic-alpine tundra (Krkonoše Mountains, Czech Republic) were selected. Throughout the growing season, UAV LiDAR point clouds (800 points/m2) and multispectral imagery (9 cm) were acquired in monthly intervals, along with reference botanical and terrestrial LiDAR data. The study provides insight into the analysis of compact low-lying vegetation at the species level. A set of experiments was conducted focusing on the analysis of LiDAR information loss, vertical strata, and structural metrics computed over the grass species/communities. Random forest was used to determine the importance of metrics by out-of-bag permutation of predictors and to classify vegetation species using UAV LiDAR-based metrics, as well as image-based digital surface models alone and in fusion with multispectral data. The vertical distribution of the UAV LiDAR points varied significantly between species and throughout the growing season. Loss at the canopy bottoms was apparent, with the lowest points corresponding to dry grass matter rather than relief. Grasslands had the highest penetration capability at the start of the growing season. In terms of metrics, maximum canopy height was the most important. The multitemporal LiDAR-derived structural metrics were able to differentiate (F-1 score above 90%) all the shrubs/trees and dominant grass Calamagrostis villosa. Mixed and low-abundance species were indistinguishable. The overall accuracy scores in a 9 (27) cm grid reached 75.1% (78.1) and 63.5% (66.4) for Bílá louka meadow and Úpské rašeliniště bog, respectively. Fusing the LiDAR-derived features with multispectral imagery did not enhance the classification results apart from the delineation of shrubs and trees.

MosViT: towards vision transformers for moving object segmentation based on Lidar point cloud

Abstract Moving object segmentation is fundamental for various downstream tasks in robotics and autonomous driving, providing crucial information for them. Effectively extracting spatial-temporal information from consecutive frames and addressing the scarcity of dataset is paramount for learning-based 3D LiDAR Moving Object Segmentation (LIDAR-MOS). In this work, we propose a novel deep neural network based on Vision Transformers (ViTs) to tackle this problem. We first validate the feasibility of Transformer networks for this task, offering an alternative to CNNs. Specifically, we utilize a dual-branch structure based on range-image data to extract spatial-temporal information from consecutive frames and fuse it using a motion-guided attention mechanism. Furthermore, we employ the ViT as the backbone, keeping its architecture unchanged from what is used for RGB images. This enables us to leverage pre-trained models from RGB images to improve results, addressing the issue of limited LIDAR point cloud data, which is cheaper compared to acquiring and annotating point cloud data. We validate the effectiveness of our approach on the LIDAR-MOS benchmark of SemanticKitti and achieve comparable results to methods that use CNNs on range image data. The source code and trained models are available at https://github.com/mafangniu/MOSViT.git.

DopplerPTNet: Object Detection Network with Doppler Velocity Information for FMCW LiDAR Point Cloud

Abstract In the field of autonomous driving, LiDAR plays a crucial role in perception and detection. LiDAR based on Time-of-Flight (ToF) mode can only provide three-dimensional spatial coordinate information of point clouds. In point cloud object detection, the limited feature information of spatial coordinates to some extent restricts the further optimization and improvement of algorithm detection performance. However, LiDAR based on Frequency-Modulated Continuous-Wave (FMCW) mode can not only obtain the three-dimensional spatial coordinates of point clouds, but also directly measure the Doppler velocity information of points, effectively compensating for the limitation of relying solely on spatial coordinate information for object recognition. Therefore, based on the CARLA simulator, we construct the first FMCW LiDAR point cloud object detection simulation dataset, FMCWLidDet. What’s more, a novel 4D object detection algorithm, DopplerPTNet, is proposed based on the direct acquisition of point Doppler velocity information by FMCW LiDAR. The algorithm solves the problem of single spatial coordinate information feature in existing 3D object detection algorithms, which makes it difficult to further improve detection accuracy. The dataset is available at https://github.com/xilight123/FMCW-LiDAR-object-detection-dataset.

Integrating vision and lidar based hyperlocal metadata for optimal capacity expansion planning in hillside road networks

The rapidly expanding population and scarcity of affordable housing in many urban areas, such as Los Angeles, have resulted in large inventories of structures in hilly landscapes. Such urbanization has also rendered road capacities inadequate in many instances, with hazards such as floods, fires, and earthquakes posing additional risks to such inventory. Planning construction activities such as widening, structural, and geotechnical retrofits to increase capacity and resiliency by cutting through slopes and incorporating landslide protection measures requires hyperlocal information for road segments in hillside areas. Performing traditional surveys for large areas of such road networks can be both time-consuming and cost prohibitive. At the same time, data acquired from satellites may not provide the required spatial resolution for planning, especially regarding road-adjacent features such as slopes. This paper proposes a novel framework to fuse hyperlocal data regarding road and adjacent slopes gathered from the street-level mobile mapper equipped with cameras and lidar into the network design problem (NDP) to develop an optimal road width expansion plan. The hyperlocal features are derived from a colorized point cloud map and deep learning techniques to estimate the relevant features in hillside landscapes. Specifically, a deep learning-based segmentation and detection algorithms combined with novel point cloud processing techniques are used to obtain a spatially dense profile of the road and the adjacent slope. By leveraging such hyperlocal information, we explore all the potential road capacity and improvement options for road capacity expansion. Furthermore, we demonstrate the advantages of our proposed method over conventional techniques, particularly working with budget constraints. In terms of novel contributions, incorporating hyperlocal information into NDP for hillside road capacity expansion is novel, as well as algorithms to automatically detect and characterize relevant roadside features from remotely sensed lidar point clouds.

A dynamic object removing 3D reconstruction system based on multi-sensor fusion

Abstract Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.

LiDARCapV2: 3D human pose estimation with human–object interaction from LiDAR point clouds

Human–object interactions in open environments are common in the real world. Estimating 3D human pose from data where objects occlude the human is a challenging task in biometrics. However, existing LiDAR-based human motion capture datasets lack occlusion scenarios between humans and objects. To overcome this limitation, we propose LiDARHuman51M, a new human–object interaction dataset captured by LiDAR in long-range outdoor scene. It includes human motion labels acquired by an IMU system and synchronous RGB images. Additionally, we present an occlusion-aware method, LiDARCapV2, for capturing human motion from LiDAR point clouds under human–object interaction settings. Our key insight is to overcome object interference in human feature extraction by introducing a module called AgNoise-Segment. A noise augmentation strategy introduced in the AgNoise-Segment module alleviates the dependency of the segmentation accuracy on the effectiveness of 3D human pose estimations. Furthermore, we propose a skeleton extraction module that integrates features learned from the AgNoise-Segment module and predicts the skeleton locations. Quantitative and qualitative experiments demonstrate that LiDARCapV2 can capture high-quality 3D human motion under human–object interaction settings. Experiments on the KITTI and Waymo datasets demonstrate that our method can be generalized to real-world open scenarios.

CL-fusionBEV: 3D object detection method with camera-LiDAR fusion in Bird’s Eye View

In the wave of research on autonomous driving, 3D object detection from the Bird’s Eye View (BEV) perspective has emerged as a pivotal area of focus. The essence of this challenge is the effective fusion of camera and LiDAR data into the BEV. Current approaches predominantly train and predict within the front view and Cartesian coordinate system, often overlooking the inherent structural and operational differences between cameras and LiDAR sensors. This paper introduces CL-FusionBEV, an innovative 3D object detection methodology tailored for sensor data fusion in the BEV perspective. Our approach initiates with a view transformation, facilitated by an implicit learning module that transitions the camera’s perspective to the BEV space, thereby aligning the prediction module. Subsequently, to achieve modal fusion within the BEV framework, we employ voxelization to convert the LiDAR point cloud into BEV space, thereby generating LiDAR BEV spatial features. Moreover, to integrate the BEV spatial features from both camera and LiDAR, we have developed a multi-modal cross-attention mechanism and an implicit multi-modal fusion network, designed to enhance the synergy and application of dual-modal data. To counteract potential deficiencies in global reasoning and feature interaction arising from multi-modal cross-attention, we propose a BEV self-attention mechanism that facilitates comprehensive global feature operations. Our methodology has undergone rigorous evaluation on a substantial dataset within the autonomous driving domain, the nuScenes dataset. The outcomes demonstrate that our method achieves a mean Average Precision (mAP) of 73.3% and a nuScenes Detection Score (NDS) of 75.5%, particularly excelling in the detection of cars and pedestrians with high accuracies of 89% and 90.7%, respectively. Additionally, CL-FusionBEV exhibits superior performance in identifying occluded and distant objects, surpassing existing comparative methods.

Intra-Frame Graph Structure and Inter-Frame Bipartite Graph Matching with ReID-Based Occlusion Resilience for Point Cloud Multi-Object Tracking

Three-dimensional multi-object tracking (MOT) using lidar point cloud data is crucial for applications in autonomous driving, smart cities, and robotic navigation. It involves identifying objects in point cloud sequence data and consistently assigning unique identities to them throughout the sequence. Occlusions can lead to missed detections, resulting in incorrect data associations and ID switches. To address these challenges, we propose a novel point cloud multi-object tracker called GBRTracker. Our method integrates an intra-frame graph structure into the backbone to extract and aggregate spatial neighborhood node features, significantly reducing detection misses. We construct an inter-frame bipartite graph for data association and design a sophisticated cost matrix based on the center, box size, velocity, and heading angle. Using a minimum-cost flow algorithm to achieve globally optimal matching, thereby reducing ID switches. For unmatched detections, we design a motion-based re-identification (ReID) feature embedding module, which uses velocity and the heading angle to calculate similarity and association probability, reconnecting them with their corresponding trajectory IDs or initializing new tracks. Our method maintains high accuracy and reliability, significantly reducing ID switches and trajectory fragmentation, even in challenging scenarios. We validate the effectiveness of GBRTracker through comparative and ablation experiments on the NuScenes and Waymo Open Datasets, demonstrating its superiority over state-of-the-art methods.

SaBi3d—A LiDAR Point Cloud Data Set of Car-to-Bicycle Overtaking Maneuvers

While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles represent a significant barrier for many potential cyclists. A large-scale analysis of overtaking maneuvers could inform traffic researchers and city planners how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and from LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, which consists of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates the detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3D object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation.

Multimodal spatiotemporal aggregation for point cloud accumulation

Point cloud accumulation is a crucial technique in point cloud analysis, facilitating various downstream tasks like surface reconstruction. Current methods merely rely on raw LiDAR points, yielding unsatisfactory performance due to the limited geometric information, particularly in complex scenarios characterized by intricate motions, diverse objects, and an increased number of frames. In this paper, we introduce camera modality data, which is usually acquired alongside LiDAR data at minimal expense. To this end, we present the Multimodal Spatiotemporal Aggregation solution (termed MSA) to thoroughly explore and aggregate these two distinct modalities (sparse 3D points and multi-view 2D images). Concretely, we propose a multimodal spatial aggregation module to bridge the data gap between different modalities in the Bird’s-Eye-View (BEV) space and further fuse them by learnable adaptive channel-wise weights. By assembling their respective strengths, this module generates a reliable and consistent scene representation. Subsequently, we design a temporal aggregation module to capture continuous motion information across consecutive sequences, which is beneficial for identifying the motion state of the foreground scene and enabling the model to extend robustly to longer sequences. Experiments demonstrate MSA outperforms state-of-the-art (SoTA) point cloud accumulation methods across all evaluation metrics in the public benchmark, especially with more frames.

LiDAR-Based 3D Temporal Object Detection via Motion-Aware LiDAR Feature Fusion.

Recently, the growing demand for autonomous driving in the industry has led to a lot of interest in 3D object detection, resulting in many excellent 3D object detection algorithms. However, most 3D object detectors focus only on a single set of LiDAR points, ignoring their potential ability to improve performance by leveraging the information provided by the consecutive set of LIDAR points. In this paper, we propose a novel 3D object detection method called temporal motion-aware 3D object detection (TM3DOD), which utilizes temporal LiDAR data. In the proposed TM3DOD method, we aggregate LiDAR voxels over time and the current BEV features by generating motion features using consecutive BEV feature maps. First, we present the temporal voxel encoder (TVE), which generates voxel representations by capturing the temporal relationships among the point sets within a voxel. Next, we design a motion-aware feature aggregation network (MFANet), which aims to enhance the current BEV feature representation by quantifying the temporal variation between two consecutive BEV feature maps. By analyzing the differences and changes in the BEV feature maps over time, MFANet captures motion information and integrates it into the current feature representation, enabling more robust and accurate detection of 3D objects. Experimental evaluations on the nuScenes benchmark dataset demonstrate that the proposed TM3DOD method achieved significant improvements in 3D detection performance compared with the baseline methods. Additionally, our method achieved comparable performance to state-of-the-art approaches.

High-throughput physiological phenotyping of crop evapotranspiration at the plot scale

ContextPlatforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. ObjectiveIn this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. MethodsThe new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. ResultsThe result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R2 values ranging from 0.35 to 0.93 (average R2 = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. ConclusionHigh spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. SignificancesOur work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation.

Enhanced Strapdown Inertial Navigation System (SINS)/LiDAR Tightly Integrated Simultaneous Localization and Mapping (SLAM) for Urban Structural Feature Weaken Occasions in Vehicular Platform

LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods.

FLApy: A Python package for evaluating the 3D light availability heterogeneity within forest communities

Abstract Light availability (LAv) dictates a variety of biological and ecological processes across a range of spatiotemporal scales. Quantifying the spatial pattern of LAv in three‐dimensional (3D) space can promote the understanding of microclimates that are critical to fine‐scale species distribution. However, there is still a lack of tools that are robust to evaluate spatiotemporal heterogeneity of LAv in forests. Here, we propose the Forest Light Analyzer python package (FLApy), an open‐source computational tool designed for the analysis of intra‐forest LAv variation across multiple spatial scales. FLApy is freely invoked by Python, facilitating the processing of LiDAR point cloud data into a 3D data container constructed by voxels, as well as traversal calculations related to the LAv regime by high performance synthetic hemispherical algorithm. Furthermore, FLApy incorporates 37 indicators, enabling users to expediently export and visualize LAv patterns and the evaluation of heterogeneity of LAv at two scales (voxel scale and 3D‐cluster scale) for a range of fine‐scale ecological study purposes. To validate the efficacy of the FLApy, we employed a simulated point cloud dataset that simulates forests (varying in canopy closure). Furthermore, to evaluate real world forest, we executed the standard workflow of FLApy utilizing drone‐derived data from three subtropical evergreen broad‐leaved forest dynamics plots within the Ailao Mountain Reserve. Our findings underscore that a series of indices derived from FLApy provide a robust characterization of light availability heterogeneity within diverse forest settings. Additionally, when juxtaposed with conventional monitoring techniques, the metrics offered by FLApy demonstrated better generality in our field assessments. FLApy offers ecologists a solution for rapid quantification of understory light 3D‐regimes across multiple scales, addressing the disparity between traditional manual approaches and the precision required for contemporary ecological studies. Moreover, FLApy provides robust support for the establishment and expansion of heterogeneity indices based on 3D micro‐environments, enhancing our understanding of the largely uncharted 3D structural patterns. Anticipated outcomes suggest that FLApy will enhance our knowledge concerning the intra‐forest climatic conditions into a 3D context, proving pivotal in the delineation of microhabitats and the development of detailed 3D‐scale species distribution models.

A study on 3D LiDAR-based point cloud object detection using an enhanced PointPillars network

Abstract The PointPillar target detection algorithm is a mainstream 3D lidar point cloud target detection algorithm that has a fast response speed but low detection accuracy. Addressing the problem of the low detection accuracy of the PointPillar target detection network, we propose an improved PointPillar target detection algorithm that integrates an attention mechanism. The algorithm first introduces the attention mechanism and strengthens the feature extraction module based on PointPillar to realize the amplification of the local information in the three scale feature maps and to better extract the more important feature information. Then, our algorithm adds an anchor free type detector head to further optimize the detector head module. The experimental results show that the optimized PointPillar target detection algorithm has achieved good test results in the KITTI data set. Under medium difficulty, the AOS mode mAP reaches 79.76%, the 3D mode mAP reaches 82.03%, and the BEV mode mAP reaches 82.30%. Compared with that of other point cloud target detection algorithms, the detection accuracy of our algorithm is improved by approximately 10%.