A research team has developed autonomous driving software that allows inexpensive sensors to detect transparent obstacles such as glass walls, providing an alternative to high-performance sensors. This technology can be used in existing robots, negating the need for additional equipment while ensuring detection performance that is equal to that offered by expensive conventional equipment.
The paper is published in the journal IEEE Transactions on Instrumentation and Measurement. The team was led by Professor Kyungjoon Park at the Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science & Technology.
Autonomous driving robots typically use LiDAR sensors to detect their surroundings and navigate. Functioning as “laser eyes,” expensive LiDAR sensors determine distance and structure by projecting light and measuring reflection time.
Inexpensive LiDAR sensors cannot detect transparent objects such as those made of glass; they may mistake them for empty space, potentially resulting in a collision. High-resolution ultrasonic LiDAR sensors or cameras do not have this limitation, but their use increases system complexity and raises costs by hundreds of thousands to millions of won.
To provide an alternative, a DGIST research team led by Professor Kyungjoon Park developed probabilistic incremental navigation-based mapping (PINMAP), an algorithm that approaches problem-solving via software, not hardware. PINMAP accumulates rare point data that inexpensive LiDAR sensors can detect only sporadically. Using these data, PINMAP probabilistically calculates the likelihood of the presence of glass walls over time.
The PINMAP algorithm is based on Cartographer (map charting) and Nav2 (navigation), which are open-source tools that are widely used in the ROS 2 ecosystem. PINMAP has the advantage of easy applicability while eliminating the need to change the existing system structure.
Instead of upgrading the sensors at a high cost, the algorithm alters the way the existing sensors handle data; that is, it uses software to improve the detection performance of inexpensive LiDAR sensors.
In a real-world experiment conducted at DGIST, PINMAP detected glass walls with 96.77% accuracy, which is well above the nearly 0% detection rate of the traditional approach using the same inexpensive LiDAR sensors (Cartographer-SLAM). The software difference that PINMAP offers demonstrated a tremendous performance boost.
Professor Park said, “PINMAP flips the conventional wisdom that hardware performance equals system performance and proposes a new standard whereby software can improve sensor capabilities. This study shows that ensuring stable autonomous driving is possible without relying on high-performance equipment.”
The algorithm the research team developed offers a substantial economic advantage because it achieves detection performance comparable to that of expensive LiDAR sensors at less than one-tenth of the cost. This technology is expected to reduce collisions between autonomous driving robots and glass or transparent acrylic walls in indoor spaces such as hospitals, airports, shopping malls, and warehouses, thus contributing to the large-scale deployment of service robots.
More information:
Jiyeong Chae et al, PINMAP: A Cost-Efficient Algorithm for Glass Detection and Mapping Using Low-Cost 2-D LiDAR, IEEE Transactions on Instrumentation and Measurement (2025). DOI: 10.1109/TIM.2025.3566826
Daegu Gyeongbuk Institute of Science and Technology
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Smart software replaces expensive sensors for glass wall detection with 96% accuracy (2025, May 30)
retrieved 30 May 2025
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