Description:FIELD OF INVENTION
The present invention relates to 3D object detection using LiDAR fusion, specifically integrating LiDAR data with other sensor modalities such as cameras and radar to enhance object recognition accuracy. It focuses on improving depth perception, spatial awareness, and real-time object classification for applications in autonomous vehicles, robotics, and surveillance systems. The invention aims to optimize sensor fusion techniques, data processing algorithms, and machine learning models to achieve robust and precise 3D object detection.
BACKGROUND OF THE INVENTION
Accurate 3D object detection is a critical requirement for autonomous systems, robotics, and advanced driver-assistance systems (ADAS). Traditional object detection techniques rely heavily on camera-based vision systems, which struggle in low-light conditions, poor weather, and occlusions. While LiDAR (Light Detection and Ranging) offers precise depth and spatial information, it lacks the rich texture and color details provided by cameras. The challenge arises in combining these diverse sensor modalities efficiently to achieve highly accurate and reliable object detection across varying environments.
Existing approaches to sensor fusion for 3D object detection often suffer from misalignment, computational inefficiency, and inaccurate feature extraction due to differences in resolution, field of view, and data formats between LiDAR and other sensors. Many methods use early or late fusion strategies that fail to fully exploit the complementary strengths of LiDAR and cameras, leading to suboptimal performance in real-world applications. Additionally, standalone LiDAR-based object detection can be computationally expensive, making real-time processing a challenge for edge computing and embedded systems.
The present invention addresses these limitations by introducing an optimized LiDAR fusion framework that leverages advanced sensor alignment, feature fusion techniques, and deep learning models to improve detection accuracy and efficiency. By integrating LiDAR with camera and radar data using adaptive fusion algorithms, this invention ensures robust detection even in challenging conditions such as fog, heavy rain, or complex urban environments. This innovation provides a scalable, high-performance solution for autonomous navigation, security surveillance, and industrial automation, making 3D object detection more precise, efficient, and reliable.
OBJECTS OF THE INVENTION
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative
An object of the present disclosure is to enhance object detection accuracy by integrating multiple sensor data sources (LiDAR, cameras, and radar).
Another object of the present disclosure is to ensure seamless data synchronization and calibration to create a unified representation of the environment.
Still another object of the present disclosure is to refine data quality through preprocessing techniques to remove noise and optimize resolution.
Another object of the present disclosure is to
Still another object of the present disclosure is to leverage deep learning models for high-precision object classification and detection.
Still another object of the present disclosure is to improve detection in complex environments by combining LiDAR, camera, and radar data for better object recognition.
Yet another object of the present disclosure is to enable real-time processing for low-latency, actionable decision-making in dynamic settings.
Yet another object of the present disclosure is to provide actionable insights such as 3D bounding boxes and trajectory predictions for autonomous systems.
Yet another object of the present disclosure is to ensure scalability and adaptability to diverse applications and dynamic environmental conditions.

Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

SUMMARY OF THE INVENTION
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.

The present invention is generally relates to the system combines LiDAR, cameras, and radar to provide complementary data, addressing each sensor's limitations for enhanced detection accuracy.
An embodiment of the present invention is data from multiple sensors are synchronized and calibrated, ensuring a unified, coherent representation of the surroundings for better object recognition.
Another embodiment of the invention is the system filters noise, optimizes point cloud resolution, and extracts key object features to improve the quality of the data before analysis.
Yet another embodiment of the invention is convolutional Neural Networks (CNNs) and Transformer-based architectures are used to analyze the fused data for high-precision object detection.
Yet another embodiment of the invention is the fusion of LiDAR’s spatial awareness, camera’s texture, and radar’s motion tracking improves detection in cluttered and occluded environments.
Yet another embodiment of the invention is high-performance computing units, such as GPUs and edge AI chips, enable fast, low-latency inference for real-time decision-making.
Yet another embodiment of the invention is the system generates 3D bounding boxes, heatmaps, and trajectory predictions to provide actionable insights for autonomous navigation or security responses.
Yet another embodiment of the invention is the system’s robust design allows it to adapt to dynamic environments, minimizing false detections and enhancing operational efficiency in real-world applications.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig 1: Flowing chart showing the Working Mechanism of the Invention
DETAILED DESCRIPTION OF THE INVENTION
The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.
The present invention relates to an advanced 3D object detection system that utilizes sensor fusion to enhance the accuracy, efficiency, and reliability of autonomous and security applications. By seamlessly integrating LiDAR, cameras, and radar, the system compensates for the individual limitations of each sensor. LiDAR provides precise depth and spatial data, cameras add texture and color, and radar offers motion tracking capabilities. The sensor fusion module synchronizes and calibrates the data, correcting discrepancies such as resolution, perspective, and time delays. This unified data representation enables more accurate object detection in complex and dynamic environments, making the system suitable for applications in autonomous navigation, security surveillance, and robotics.

The system's deep learning-based detection models, including Convolutional Neural Networks (CNNs) and Transformer-based architectures, analyze the fused data to classify objects with high precision. These models leverage spatial and semantic relationships to identify vehicles, pedestrians, and obstacles, even in cluttered or occluded settings. Real-time processing, powered by high-performance computing units like GPUs or edge AI chips, allows for rapid inference and low-latency object recognition. The final output, such as 3D bounding boxes, heatmaps, or trajectory predictions, provides actionable insights that guide control systems and automated responses, ensuring efficient and intelligent decision-making in real-world scenarios. This invention represents a significant advancement in 3D object detection, enabling more reliable and adaptable systems for a variety of use cases.

LiDAR Sensor Module
The LiDAR sensor serves as the primary data acquisition unit, providing high-resolution depth information by emitting laser pulses and measuring the time it takes for them to return after hitting objects. This component generates a 3D point cloud, offering precise spatial awareness of the surroundings. Depending on the application, the system can utilize solid-state LiDAR, spinning LiDAR, or flash LiDAR to achieve different levels of range, resolution, and field of view. The LiDAR module is designed to work in varying environmental conditions, ensuring robust object detection even in low-light or adverse weather scenarios.
Camera Sensor Module
The camera module complements the LiDAR sensor by providing color, texture, and object appearance details, which are crucial for accurate classification and recognition. High-resolution monocular, stereo, or RGB-D cameras can be integrated into the system to capture detailed visual information. The camera data helps in identifying object categories, reading signs, and improving depth estimation when fused with LiDAR data. To enhance performance in different lighting conditions, the camera module may include infrared (IR) or night vision capabilities.
Radar Sensor Module (Optional)
In some applications, radar sensors are incorporated to provide additional information about an object's velocity and motion trajectory. Unlike LiDAR and cameras, radar is less affected by weather conditions like fog, rain, or dust, making it a valuable addition for autonomous vehicles and surveillance applications. The radar data helps in tracking moving objects, reducing false positives, and improving overall system reliability in dynamic environments.
Sensor Fusion and Calibration Unit
The sensor fusion module is a crucial component that synchronizes data from LiDAR, cameras, and radar, ensuring accurate alignment and meaningful integration of multimodal information. This unit employs calibration algorithms to correct for differences in sensor placement, resolution, and time delays. It can use extrinsic and intrinsic calibration techniques, deep learning-based fusion models, or geometric transformation methods to accurately overlay LiDAR point clouds with 2D image data, enhancing object detection accuracy.
Data Processing and Feature Extraction Unit
The raw data collected from sensors undergoes pre-processing and feature extraction to identify relevant information for 3D object detection. This unit filters out noisy, irrelevant, or redundant data using algorithms like voxelization, clustering, and denoising. Deep learning models such as PointNet, VoxelNet, or Transformer-based architectures are applied to extract spatial and semantic features from point clouds and images, enabling precise identification of objects, their boundaries, and their movement patterns.
Neural Network-Based Detection Model
A deep learning-based detection model forms the core of the invention, utilizing convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformer models to analyze fused sensor data. These models classify and localize objects in 3D space by learning spatial relationships and object characteristics. Advanced techniques like graph neural networks (GNNs), attention mechanisms, or self-supervised learning may be employed to improve detection accuracy, reduce computation time, and enhance generalization across different environments.
Real-Time Processing and Edge Computing Unit
To ensure real-time 3D object detection, the system is integrated with high-performance computing hardware such as GPUs, TPUs, or FPGAs for accelerated inference. In edge computing applications, specialized low-power AI chips optimize performance for embedded systems like autonomous vehicles, drones, and robotic systems. This unit ensures efficient processing of sensor data, enabling instant decision-making and responsive action in dynamic environments.
Output Interface and Decision-Making Module
The final component of the invention is the output interface, which communicates detection results to the user or the control system. This can be in the form of 3D bounding boxes, heatmaps, or object tracking visualizations. In autonomous systems, this module interfaces with control units to enable path planning, collision avoidance, or automated navigation. For applications in security surveillance, the output can trigger alerts, alarms, or automatic recording systems based on detected threats or anomalies.
EXAMPLE 2; Working Mechanism
The 3D object detection system using LiDAR fusion functions through the seamless integration of multiple sensors, data processing units, and deep learning models, each playing a crucial role in enhancing accuracy, efficiency, and reliability. The process begins with multi-sensor data acquisition, where LiDAR captures precise depth and spatial data, cameras provide texture and color information, and radar (if included) offers velocity and motion tracking. These sensors complement each other, addressing the limitations of individual systems—LiDAR’s lack of texture is compensated by the camera’s rich visual details, while radar ensures effective tracking in poor weather conditions. The sensor fusion module synchronizes and calibrates data, correcting discrepancies in resolution, perspective, and time delays, ensuring a unified and coherent representation of the surroundings.
Once the data is aligned, the preprocessing and feature extraction unit refines the information by filtering out noise, optimizing point cloud resolution, and identifying key object characteristics. The system then applies deep learning-based detection models, such as Convolutional Neural Networks (CNNs) or Transformer-based architectures, to analyze the fused data and classify objects with high precision. The neural network leverages spatial and semantic relationships, allowing for improved identification of objects, even in cluttered or occluded environments. By combining LiDAR’s 3D spatial awareness with camera-based classification and radar’s motion tracking, the model enhances the detection of vehicles, pedestrians, and obstacles, making it highly effective for applications like autonomous navigation, security surveillance, and robotic vision.
The final stage involves real-time processing and decision-making, where high-performance computing units, such as GPUs or edge AI chips, enable rapid inference and low-latency object recognition. The system generates 3D bounding boxes, heatmaps, or trajectory predictions, providing actionable insights for control systems in autonomous vehicles or security applications. The output interface then transmits detection results to navigation modules, alert mechanisms, or automated response systems, ensuring smooth and intelligent decision-making.
While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:1. A 3D object detection system using LiDAR fusion, comprising:
a) a lidar sensor configured to generate a three-dimensional (3d) point cloud representing the spatial structure of the environment;
b) at least one camera sensor to capture two-dimensional (2d) image data for texture, color, and object recognition;
c) a sensor fusion module that aligns and integrates lidar point cloud data with camera image data to enhance object detection accuracy;
d) a data preprocessing unit that filters noise, optimizes resolution, and extracts key object features from the fused data;
e) a deep learning-based detection module utilizing neural networks to classify and localize objects in 3d space; and
f) a real-time processing unit that analyzes the detected objects and transmits information to an output interface for autonomous navigation, surveillance, or other applications,
wherein the system enhances depth perception, spatial awareness, and object classification accuracy by combining lidar and camera data through advanced fusion techniques.
2. The system as claimed in claim 1, wherein the sensor fusion module uses extrinsic and intrinsic calibration to align LiDAR point clouds with camera images, correcting discrepancies in resolution, field of view, and perspective.
3. The system as claimed in claim 1, wherein the deep learning-based detection module employs a convolutional neural network (CNN), transformer network, or graph neural network (GNN) to improve object recognition and classification.
4. The system as claimed in claim 1, wherein an optional radar sensor is integrated to provide motion tracking and velocity estimation, further enhancing object detection in dynamic environments.
5. The system as claimed in claim 1, wherein the data preprocessing unit applies voxelization, clustering, and filtering techniques to reduce noise and improve the quality of LiDAR and camera fusion data.
6. The system as claimed in claim 1, wherein the real-time processing unit comprises high-performance computing hardware such as GPUs, TPUs, or edge AI processors, enabling low-latency object detection.
7. The system as claimed in claim 1, wherein the output interface provides 3D bounding boxes, semantic segmentation maps, or trajectory predictions for use in autonomous driving, robotics, or security applications.
8. The system as claimed in claim 1, wherein sensor fusion is performed at multiple stages, including early fusion, mid-level fusion, and late fusion, optimizing the detection accuracy based on environmental conditions.
Dated this 19 February 2025

Dr. Amrish Chandra
Agent of the applicant
IN/PA No: 2959