LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Tibebu, Haileleol; Roche, Jamie; Silva, Varuna De; Kondoz, A.M.

doi:10.3390/s21072263

Cited by 26 publications

(14 citation statements)

References 25 publications

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“…Another school of thought is to utilize the color channels in images and intensity channels in LIDARs to maximize the mutual information for calibration purposes [20]. However, depending on the viewing angle, the intensity return from the LIDAR could be drastically different from the image outcomes [21].…”

Section: Related Workmentioning

confidence: 99%

SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

Kodaira¹,

Zhou²,

Zang³

et al. 2022

Preprint

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With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their singlemodality counterparts in robotics. Camera and LIDAR, with complementary semantic and depth information, are the typical choices for detection tasks in complicated driving environments. For most camera-LIDAR fusion algorithms, however, the calibration of the sensor suite will greatly impact the performance. More specifically, the detection algorithm usually requires an accurate geometric relationship among multiple sensors as the input, and it is often assumed that the contents from these sensors are captured at the same time. Preparing such sensor suites involves carefully designed calibration rigs and accurate synchronization mechanisms, and the preparation process is usually done offline. In this work, a segmentationbased framework is proposed to jointly estimate the geometrical and temporal parameters in the calibration of a camera-LIDAR suite. A semantic segmentation mask is first applied to both sensor modalities, and the calibration parameters are optimized through pixel-wise bidirectional loss. We specifically incorporated the velocity information from optical flow for temporal parameters. Since supervision is only performed at the segmentation level, no calibration label is needed within the framework. The proposed algorithm is tested on the KITTI dataset, and the result shows an accurate real-time calibration of both geometric and temporal parameters.

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Section: Related Workmentioning

confidence: 99%

SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

Kodaira¹,

Zhou²,

Zang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Vibration due to collisions with walls and other obstacles is an indicator of hazardous operation. It may arise due to the failure or malfunction of obstacle avoidance sensors or the misalignment of safety sensors, or the absence of hazardous object registration in the robot navigation map, such as tiny objects and glass walls (LiDAR sensor is sometimes not accurate in detecting tiny objects below object detection range or glass walls in [ 31 , 32 , 33 ]). A structural imbalance vibration signal is another indicator for both performance degradation and a hazardous operational environment.…”

Section: Problem Definitionmentioning

confidence: 99%

AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Pookkuttath

Elara

Sivanantham

et al. 2021

Sensors

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Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.

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“…Environment mapping considering robot motion constraints has become a current research hotspot. At present, the map types mainly include three types: metric map [3], topological map [4].…”

Section: Environment Presentationmentioning

confidence: 99%

Map Construction Fusing Environmental Information and Motion Constraints

Sun

Fan

Qiu³

et al. 2021

Proceedings of 2021 Chinese Intelligent Automation Conference

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Robots are expected to replace humans in performing scientific expedition tasks, which greatly reduces the essential impact of human basic survival on scientific expedition activities, and improve the space-time coverage of scientific expedition activities in polar regions. However, the polar regions have complex topographic features, such as the edge of the glacier on the Qinghai-Tibet Plateau is steep and the inland is relatively flat, which undoubtedly brings great challenges to realize the safe and autonomous navigation of unmanned ground vehicles. For glacier area of the Qinghai-Tibet Plateau, accurately converting environmental information into the map that robots can understand is an important part of path planning, that is, mapping. This paper proposes a method of constructing an occupancy grid map for large-scale point cloud data, taking into account the movement constraints and terrain information of the mobile platform. By dividing and rasterizing the sub-point cloud, the state of the raster can be judged according to the normal, elevation and other information. Finally, we carried out path planning based on the A* on the raster map.

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LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Cited by 26 publications

References 25 publications

SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera

AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Map Construction Fusing Environmental Information and Motion Constraints

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