Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques

Abstract

Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle’s environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.

Enhancing Indoor Robot Pedestrian Detection Using Improved PIXOR Backbone and Gaussian Heatmap Regression in 3D LiDAR Point Clouds

“…The synergy of fusing camera and LiDAR data is an area of intense research, seeking to merge the strengths of both modalities for superior 3D detection capabilities [44]. Despite the strides made in this integration, the passive nature of cameras means they still cannot match the depth-sensing fidelity of active systems like LiDAR and radar on their own [45]. The pursuit of refining passive camera-based detection to match the efficacy of active sensing technologies remains a dynamic and evolving field within computer vision.…”

Review of key technologies for warehouse 3D reconstruction

“…3D laser scanning uses the Lidar to project laser beams onto the surface and uses triangulation to measure the reflected laser beams for reconstructing of a dense 3D point cloud. Although commercial 3D laser scanners have very high accuracy, the high price greatly limits their application [37].…”