We printed a checkerboard with a 9x10 grid of blocks, each measuring 87 mm x 87 mm. Parameter V alue Model Architecture Panoptic-PolarNet Test Batch Size 2 V al Batch Size 2 Test Batch size 1 post proc threshold 0.1 post proc nms kernel 5 post proc top k 100 center loss MSE offset loss L1 center loss weight 100 offset loss weight 10 enable SAP True SAP start epoch 30 SAP rate 0.01 Table 3: Parameters for Panoptic Segmentation model Parameter V alue(s) Model Architecture 4D-StOP Learning Rate 0.0005 Momentum 0.98 Stride 1 Max in points 5000 Sampling importance Decay Sampling None Input Threads 16 Checkpoint Gap 100 Table 4: Parameters for the 4D Panoptic Segmentation model The results reveal a significant variance in performance across different categories. Notably, 'Structure' and'Ground' both achieved high mIoU at Result The results are shown in Table 8. presents the mean intersection-over-union (mIoU) percent-56 Notably, 'Structure' achieved the highest mIoU at'General Objects' category have the lowest mIoU, The dataset is divided into 17 and 6 categories, respectively. Ground' and'Roads', as opposed to grouping anything related to ground as a single category. Overall, the performance across these tasks underscores the challenges posed by our dataset's With our dataset, future work can focus on improving the model's capacity to handle such diverse The raw data, processed data, and framework code can be found on our website.

In this paper a new method for geometric robot calibration is introduced, which uses a calibration plate with precisely known distances between its measuring points. The relative measurement between two points on the calibration plate is used to determine predefined error parameters of the system. In comparison to conventional measurement methods, like laser tracker or motion capture systems, the calibration plate provides a more mechanically robust and cheaper alternative, which is furthermore easier to transport due to its small size. The calibration method, the plate design, the mathematical description of the error system as well as the identification of the parameters are described in detail. For identifying the error parameters, the least squares method and a constrained optimization problem are used. The functionality of this method was demonstrated in experiments that led to promising results, correlated with one of a laser tracker calibration. The modeling and identification of the error parameters is done for a gantry machine, but is not restricted to that type of robot.

We printed a checkerboard with a 9x10 grid of blocks, each measuring 87 mm x 87 mm. Parameter V alue Model Architecture Panoptic-PolarNet Test Batch Size 2 V al Batch Size 2 Test Batch size 1 post proc threshold 0.1 post proc nms kernel 5 post proc top k 100 center loss MSE offset loss L1 center loss weight 100 offset loss weight 10 enable SAP True SAP start epoch 30 SAP rate 0.01 Table 3: Parameters for Panoptic Segmentation model Parameter V alue(s) Model Architecture 4D-StOP Learning Rate 0.0005 Momentum 0.98 Stride 1 Max in points 5000 Sampling importance Decay Sampling None Input Threads 16 Checkpoint Gap 100 Table 4: Parameters for the 4D Panoptic Segmentation model The results reveal a significant variance in performance across different categories. Notably, 'Structure' and'Ground' both achieved high mIoU at Result The results are shown in Table 8. presents the mean intersection-over-union (mIoU) percent-56 Notably, 'Structure' achieved the highest mIoU at'General Objects' category have the lowest mIoU, The dataset is divided into 17 and 6 categories, respectively. Ground' and'Roads', as opposed to grouping anything related to ground as a single category. Overall, the performance across these tasks underscores the challenges posed by our dataset's With our dataset, future work can focus on improving the model's capacity to handle such diverse The raw data, processed data, and framework code can be found on our website.

Abstract-- We present Phaser, a flexible system that directs narrow-beam laser light to moving robots for concurrent wireless power delivery and communication. We design a semiautomatic calibration procedure to enable fusion of stereo-vision-based 3D robot tracking with high-power beam steering, and a low-power optical communication scheme that reuses the laser light as a data channel. We fabricate a Phaser prototype using off-the-shelf hardware and evaluate its performance with battery-free autonomous robots. We demonstrate Phaser fully powering gram-scale battery-free robots to nearly 2x higher speeds than prior work while simultaneously controlling them to navigate around obstacles and along paths. Code, an open-source design guide, and a demonstration video of Phaser is available at https: //mobilex.cs.columbia.edu/phaser/. Mobile, autonomous robots play an increasingly important role in today's world, with the potential to perform tasks in warehouses, factories, and homes and conduct advanced environmental explorations [1]. However, the significant power needed for locomotion, on-board computation, and communication presents a key barrier to the broader deployment of such robots. Given the energy density of current batteries [2], most autonomous robots today either remain tethered by charging wires or must routinely return to charging stations, reducing deployment time. This problem is exacerbated in miniaturized robots, which cannot support the 100s of milligrams of battery payload [3]-[7] needed for extended operation, even on their milliwatt power budgets.

In this paper, a novel prototype for harvesting table-top grown strawberries is presented, that is minimalist in its footprint interacting with the fruit. In our methodology, a smooth trapper manipulates the stem into a precise groove location at which a distant laser beam is focused. The tool reaches temperatures as high as 188° Celsius and as such killing germs and preventing the spread of local plant diseases. The burnt stem wound preserves water content and in turn the fruit shelf life. Cycle and cut times achieved are 5.56 and 2.88 seconds respectively in successful in-door harvesting demonstration. Extensive experiments are performed to optimize the laser spot diameter and lateral speed against the cutting time.

Abstract-- In this paper, a novel tool prototype for harvesting table-top grown strawberries is presented. With robustness against strawberry localization error of 15mm and average cycle time of 8.02 seconds at 50% of maximum operational velocity, it provides a promising contribution towards full automation of strawberry harvesting. In addition, the tool has an overall fruit-interacting width of 35mm that greatly enhances reach-ability due to the minimal footprint. A complete harvesting system is also proposed that can be readily mounted to a mobile platform for field tests. An experimental demonstration is performed to showcase the new methodology and derive relevant metrics.

Micro scanning mirrors (MSM) extend the range and field of view of LiDARs, medical imaging devices, and laser projectors. However, a new class of soft-hinged MSMs contains out-of-plane translation in addition to the 2 degree-of-freedom rotations, which presents a cabliration challenge. We report a new calibration system and algorithm design to address the challenge. In the calibration system, a new low-cost calibration rig design employs a minimal 2-laser beam approach. The new new algorithm builds on the reflection principle and an optimization approach to precisely measure MSM poses. To establish the mapping between Hall sensor readings and MSM poses, we propose a self-synchronizing periodicity-based model fitting calibration approach. We achieve an MSM poses estimation accuracy of 0.020{\deg} with a standard deviation of 0.011{\deg}.

Most commercially available Light Detection and Ranging (LiDAR)s measure the distances along a 2D section of the environment by sequentially sampling the free range along directions centered at the sensor's origin. When the sensor moves during the acquisition, the measured ranges are affected by a phenomenon known as "skewing", which appears as a distortion in the acquired scan. Skewing potentially affects all systems that rely on LiDAR data, however, it could be compensated if the position of the sensor were known each time a single range is measured. Most methods to de-skew a LiDAR are based on external sensors such as IMU or wheel odometry, to estimate these intermediate LiDAR positions. In this paper, we present a method that relies exclusively on range measurements to effectively estimate the robot velocities which are then used for de-skewing. Our approach is suitable for low-frequency LiDAR where the skewing is more evident. It can be seamlessly integrated into existing pipelines, enhancing their performance at a negligible computational cost.

Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods. In this work, we study the underexplored semi-supervised learning (SSL) in LiDAR segmentation. Our core idea is to leverage the strong spatial cues of LiDAR point clouds to better exploit unlabeled data. We propose LaserMix to mix laser beams from different LiDAR scans, and then encourage the model to make consistent and confident predictions before and after mixing. Our framework has three appealing properties: 1) Generic: LaserMix is agnostic to LiDAR representations (e.g., range view and voxel), and hence our SSL framework can be universally applied. 2) Statistically grounded: We provide a detailed analysis to theoretically explain the applicability of the proposed framework. 3) Effective: Comprehensive experimental analysis on popular LiDAR segmentation datasets (nuScenes, SemanticKITTI, and ScribbleKITTI) demonstrates our effectiveness and superiority. Notably, we achieve competitive results over fully-supervised counterparts with 2x to 5x fewer labels and improve the supervised-only baseline significantly by 10.8% on average. We hope this concise yet high-performing framework could facilitate future research in semi-supervised LiDAR segmentation. Code is publicly available.

Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modelity in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker method, where the ultrasound / photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument Methods: An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principles of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot. Results: The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics. The result is also comparable with our previous approach, and the proposed method can be implemented with a normal white light stereo camera and doesn't require highly accurate localization of the PM. Conclusion: The proposed frame registration algorithm enabled a simple yet efficient integration of commercial US/PA imaging system into laparoscopic surgical setting by leveraging the characteristic properties of acoustic wave propagation and laser excitation, contributing to automated US/PA image-guided surgical intervention applications.