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Here we propose a real-time method for low-drift odometry and mapping using range measurements from a 3D laser scanner moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation (especially without an external reference such as GPS) cause mis-registration of the resulting point cloud. To date, coherent 3D maps have been built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift in motion estimation and low-computational complexity. The key idea that makes this level of performance possible is the division of the complex problem of Simultaneous Localization and Mapping, which seeks to optimize a large number of variables simultaneously, into two algorithms. One algorithm performs odometry at a high-frequency but at low fidelity to estimate velocity of the laser scanner. Although not necessary, if an IMU is available, it can provide a motion prior and mitigate for gross, high-frequency motion. A second algorithm runs at an order of magnitude lower frequency for fine matching and registration of the point cloud. Combination of the two algorithms allows map creation in real-time. Our method has been evaluated by indoor and outdoor experiments as well as the KITTI odometry benchmark. The results indicate that the proposed method can achieve accuracy comparable to the state of the art offline, batch methods.

Herein, point light detection and ranging inertial odometry (LIO) is presented: a robust and high‐bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate extremely aggressive robotic motions. Point‐LIO has two key novelties. The first one is a point‐by‐point LIO framework that updates the state at each LiDAR point measurement. This framework allows an extremely high‐frequency odometry output, significantly increases the odometry bandwidth, and fundamentally removes the artificial in‐frame motion distortion. The second one is a stochastic process‐augmented kinematic model which models the IMU measurement as an output. This new modeling method enables accurate localization and reliable mapping for aggressive motions even with inertial measurement unit (IMU) measurements saturated in the middle of the motion. Various real‐world experiments are conducted for performance evaluation. Overall, Point‐LIO is capable to provide accurate, high‐frequency odometry (4–8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s−1) beyond the IMU measuring ranges. Furthermore, an exhaustive benchmark comparison is conducted. Point‐LIO achieves consistently comparable accuracy and time consumption. Finally, two example applications of Point‐LIO are demonstrated, one is a racing drone and the other is a self‐rotating unmanned aerial vehicle, both have aggressive motions. A high‐bandwidth light detection and ranging (LiDAR)‐inertial system (LIO) is presented. The system updates the state at the sampling time of each LiDAR point or inertial measurement unit (IMU) measurement without accumulating a frame. The system is able to output a high‐rate (4–8 kHz), high‐bandwidth (over 150 Hz) odometry and handles extremely aggressive motions where IMU saturates.

We compute the homology groups of transformation groupoids associated with odometers and show that certain $(\\mathbb{Z}\\rtimes \\mathbb{Z}_{2})$-odometers give rise to counterexamples to the HK conjecture, which relates the homology of an essentially principal, minimal, ample groupoid $G$ with the K-theory of $C_{r}^{\\ast }(G)$. We also show that transformation groupoids of odometers satisfy the AH conjecture.

In the realm of computer vision and robotics, lidar SLAM is an important technical means. Lidar SLAM frequently serves to ascertain the spatial coordinates and orientation of mobile robots amidst their surroundings. In this paper, an adaptive factor graph optimization of the lidar pose estimation method using only point-to-line constraints is proposed. The algorithm only employs the measure of distance between a point and a line to construct the constraint problem and solves the pose through point-to-line ICP and the pose with higher accuracy by optimizing the factor graph. The robustness of mobile robots in indoor scenes is improved. According to different application scenarios, including street and gate, comparative experiments are carried out on the open-source data set M2DGR containing lidar data. Empirical findings demonstrate that the algorithm put forth enhances the precision of the lidar odometer when confronted with outdoor scenarios, thereby bolstering the resilience of mobile robots traversing urban thoroughfares.

Many written forms required by businesses and governments rely on honest reporting. Proof of honest intent is typically provided through signature at the end of, e.g., tax returns or insurance policy forms. Still, people sometimes cheat to advance their financial self-interests—at great costs to society. We test an easy-to-implement method to discourage dishonesty: signing at the beginning rather than at the end of a self-report, thereby reversing the order of the current practice. Using laboratory and field experiments, we find that signing beforerather than afterthe opportunity to cheat makes ethics salient when they are needed most and significantly reduces dishonesty.

Diet-induced obesity (DIO) is a significant health concern which has been linked to structural and functional changes in the gut microbiota. Exercise (Ex) is effective in preventing obesity, but whether Ex alters the gut microbiota during development with high fat (HF) feeding is unknown. Determine the effects of voluntary Ex on the gastrointestinal microbiota in LF-fed mice and in HF-DIO. Male C57BL/6 littermates (5 weeks) were distributed equally into 4 groups: low fat (LF) sedentary (Sed) LF/Sed, LF/Ex, HF/Sed and HF/Ex. Mice were individually housed and LF/Ex and HF/Ex cages were equipped with a wheel and odometer to record Ex. Fecal samples were collected at baseline, 6 weeks and 12 weeks and used for bacterial DNA isolation. DNA was subjected both to quantitative PCR using primers specific to the 16S rRNA encoding genes for Bacteroidetes and Firmicutes and to sequencing for lower taxonomic identification using the Illumina MiSeq platform. Data were analyzed using a one or two-way ANOVA or Pearson correlation. HF diet resulted in significantly greater body weight and adiposity as well as decreased glucose tolerance that were prevented by voluntary Ex (p<0.05). Visualization of Unifrac distance data with principal coordinates analysis indicated clustering by both diet and Ex at week 12. Sequencing demonstrated Ex-induced changes in the percentage of major bacterial phyla at 12 weeks. A correlation between total Ex distance and the ΔCt Bacteroidetes: ΔCt Firmicutes ratio from qPCR demonstrated a significant inverse correlation (r2 = 0.35, p = 0.043). Ex induces a unique shift in the gut microbiota that is different from dietary effects. Microbiota changes may play a role in Ex prevention of HF-DIO.

A pipeline measurement robot(PMR) is an important tool for pipeline shape measurement and disease detection. Flexible pipelines in the horizontal plane inside dams are used for internal deformation monitoring, which is characterized by small caliber, no ground marking assistance, and higher accuracy requirements, so the accuracy of the data fusion algorithm needs to be improved. In this paper, we propose a pipeline inertial measurement odometry correction method based on pipeline junction detection, which establishes a reference model of pipeline junction locations, detects pipeline junction gap locations along the measurement route using k-mean clustering, corrects odometer data based on the difference between the detected and reference locations, fuses IMU and odometer data and evaluates the internal compliance accuracy. The proposed method is validated using the measured data of the internal pipeline of Tianchi Dam, and the results show that the method proposed in this paper reduces the root-mean-square error (RMSE) by about 46% compared with the results without detection correction and direct fusion. Therefore, the method has good results and practical applications.

Reliable and robust self-localization is the essential component of intelligent vehicles (IV). Many scholarly works have been focused on developing accurate multi-modal integrated pose estimation schemes. Such single estimation engine design lacks consideration of potential individual sensor failures. In this paper, we present a resilient framework that exploits the redundancy of different sensors using a stack of odometry algorithms. The multiple pose estimation algorithms run in parallel with a general adaptivity and lightweight design. Specifically, we integrate the vehicle wheel encoder and the vehicle dynamics data to the filter-based LiDAR-inertial odometry. In contrast to most of the odometry algorithms which may fail entirely against temporary failures, the redundant system enables self-recovery of individual odometry through reinitialization. The most promising odometry is selected at each timestamp through weighting metric evaluation. In this way, our method can exploit the robustness and advantages of individual estimating engines. We evaluate our method on both research purpose IVs and mass-produced IVs. The experimental results suggest that our approach is resilient to various failure cases and achieves better performance than individual methods.

Event-based cameras are a new type of vision sensor in which pixels operate independently and respond asynchronously to changes in brightness with microsecond resolution, instead of providing standard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities for robots to deal with some challenging scenes. We propose a visual–inertial odometry for stereo event-based cameras based on Error-State Kalman Filter (ESKF). The vision module updates the pose by relying on the edge alignment of a semi-dense 3D map to a 2D image, while the IMU module updates the pose using median integration. We evaluate our method on public datasets with general 6-DoF motion (three-axis translation and three-axis rotation) and compare the results against the ground truth. We compared our results with those from other methods, demonstrating the effectiveness of our approach.

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.