A analysis workforce has developed autonomous driving software program that enables cheap sensors to detect clear obstacles resembling glass partitions, offering an alternative choice to high-performance sensors. This expertise can be utilized in present robots, negating the necessity for extra gear whereas guaranteeing detection efficiency that is the same as that supplied by costly standard gear.
The paper is revealed within the journal IEEE Transactions on Instrumentation and Measurement. The workforce was led by Professor Kyungjoon Park on the Division of Electrical Engineering and Laptop Science, Daegu Gyeongbuk Institute of Science & Know-how.
Autonomous driving robots sometimes use LiDAR sensors to detect their environment and navigate. Functioning as “laser eyes,” costly LiDAR sensors decide distance and construction by projecting mild and measuring reflection time.
Cheap LiDAR sensors can not detect clear objects resembling these manufactured from glass; they might mistake them for empty area, probably leading to a collision. Excessive-resolution ultrasonic LiDAR sensors or cameras should not have this limitation, however their use will increase system complexity and raises prices by tons of of hundreds to tens of millions of gained.
To offer another, a DGIST analysis workforce led by Professor Kyungjoon Park developed probabilistic incremental navigation-based mapping (PINMAP), an algorithm that approaches problem-solving by way of software program, not {hardware}. PINMAP accumulates uncommon level information that cheap LiDAR sensors can detect solely sporadically. Utilizing these information, PINMAP probabilistically calculates the chance of the presence of glass partitions over time.
The PINMAP algorithm relies on Cartographer (map charting) and Nav2 (navigation), that are open-source instruments which might be extensively used within the ROS 2 ecosystem. PINMAP has the benefit of straightforward applicability whereas eliminating the necessity to change the prevailing system construction.
As an alternative of upgrading the sensors at a excessive value, the algorithm alters the way in which the prevailing sensors deal with information; that’s, it makes use of software program to enhance the detection efficiency of cheap LiDAR sensors.
In a real-world experiment carried out at DGIST, PINMAP detected glass partitions with 96.77% accuracy, which is effectively above the almost 0% detection fee of the normal method utilizing the identical cheap LiDAR sensors (Cartographer-SLAM). The software program distinction that PINMAP presents demonstrated an incredible efficiency enhance.
Professor Park stated, “PINMAP flips the standard knowledge that {hardware} efficiency equals system efficiency and proposes a brand new customary whereby software program can enhance sensor capabilities. This examine exhibits that guaranteeing steady autonomous driving is feasible with out counting on high-performance gear.”
The algorithm the analysis workforce developed presents a considerable financial benefit as a result of it achieves detection efficiency akin to that of costly LiDAR sensors at lower than one-tenth of the fee. This expertise is predicted to cut back collisions between autonomous driving robots and glass or clear acrylic partitions in indoor areas resembling hospitals, airports, buying malls, and warehouses, thus contributing to the large-scale deployment of service robots.
Extra data:
Jiyeong Chae et al, PINMAP: A Price-Environment friendly Algorithm for Glass Detection and Mapping Utilizing Low-Price 2-D LiDAR, IEEE Transactions on Instrumentation and Measurement (2025). DOI: 10.1109/TIM.2025.3566826
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Sensible software program replaces costly sensors for glass wall detection with 96% accuracy (2025, Might 30)
retrieved 30 Might 2025
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