Canadian Consulting Engineer

RoboSense launches intelligent autonomous driving LiDAR system

September 21, 2018

The RS-IPLS Intelligent Perception LiDAR system is the first hardware and software algorithm based solution for the mass production of safer autonomous cars.


RoboSense RS-IPLS display.

RoboSense, has announced its RS-IPLS Intelligent Perception LiDAR system, the first hardware and software algorithm based solution for the mass production of safer autonomous cars.

A high performance autonomous driving system, the RS-IPLS features real-time data pre-processing and a “gaze” function similar to human eyes. The RoboSense system is also 1/400th the price of traditional 64-line LiDAR systems, and is designed for the mass production of vehicles at a low price.

Based on high-performance MEMS solid-state LiDAR, the RS-IPLS system outputs high resolution colour point cloud data by merging the underlying hardware of 2D imagery with the RoboSense RS-LiDAR-Algorithm deep learning sensing algorithm developed specifically for autonomous driving. The detection algorithm achieves target level information that adjusts the Region of Interest (ROI) detection area in real-time with no delay.

The algorithm is based on the RoboSense team’s 10 years of research and technology experience in the field of LiDAR environment-aware algorithms.

When the system’s field of view perceives a target of interest, it initiates a “gaze” processing mechanism that instantly locks the target for ROI processing, achieving clear and stable environmental data. The LiDAR under-the-system architecture maintains a high degree of vigilance of the surrounding environment, constantly capturing the areas of ​​interest, allowing the “gaze” to transfer efficient and high-quality feedback in the field of view.

The RoboSense RS-IPLS Intelligent Perception LiDAR system also provides richer three-dimensional spatial data information (X, Y, Z, R, G, B) in real-time from the bottom layer, which reduces the time delay normally caused by external fusion. Data pre-processing is performed by the AI ​​algorithm, with the area of interest repeatedly detected for farther detection distance and more accurate perception results for autonomous driving, with reduced data processing stress to the central data processing unit.



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