Fusion of Sequential Information for Semantic Grid Map Estimation

In this work, we improve the semantic segmentationof multi-layer top-view grid maps in the context of LiDAR-based perception for autonomous vehicles. To achieve thisgoal, we fuse sequential information from multiple consecu-tive lidar measurements with respect to the driven trajectoryof an autonomous vehicle. By doing so, we enrich the multi-layer grid maps which are subsequently used as the input ofa neural network. Our approach can be used for LiDAR-only360◦surround view semantic scene segmentation while beingsuitable for real-time critical systems. We evaluate the bene-fit of fusing sequential information based on a dense groundtruth and discuss the effect on different semantic classes

TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis

In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environments without any calibration targets for automated driving vehicles. Since LiDAR and stereo camera are widely used for sensor data fusion on automated driving vehicles, their extrinsic calibration is highly important. However, most of the LiDAR and stereo camera calibration approaches are mainly target-based and therefore time consuming. Even the newly developed targetless approaches in last years are either inaccurate or unsuitable for driving platforms. To address those problems, we introduce TEScalib. By applying a 3D mesh reconstruction-based point cloud registration, the geometric information is used to estimate the LiDAR to stereo camera extrinsic parameters accurately and robustly. To calibrate the stereo camera, a photometric error function is builded and the LiDAR depth is involved to transform key points from one camera to another. During driving, these two parts are processed iteratively. Besides that, we also propose an uncertainty analysis for reflecting the reliability of the estimated extrinsic parameters. Our TEScalib approach evaluated on the KITTI dataset achieves very promising results

Variation in Aggressive Behavior in Sally Lightfoot Crabs (Grapsus grapsus) Relative to Age Class

Three beach locations in San Cristóbal, Galápagos Islands, Ecuador were examined to observe aggressive behavior of Grapsus grapsus. Sally lightfoot crabs play a significant role in the intertidal ecosystem, so understanding factors that influence population dynamics is necessary to ensure equilibrium. With regards to age, it is hypothesized that variation in aggressive behavior is present between age groups of Sally lightfoot crabs. It is predicted that intermediate-aged crabs will display aggressive acts most often. Also, crab distribution by age varies with distance from water, and it is predicted that larger crabs will be more prevalent in the wet zones. Further, we hypothesize that aggression in Sally lightfoot crabs varies with level of activity by outside factors, and it is predicted the crabs inhabiting beaches of higher activity levels will display more acts of aggression. Three beach locations were analyzed one hour before and one hour after low tide for aggressive behavior such as chasing or physical contact between two or more crabs. A Chi-square test was used to determine significance of data collected. The majority of acts of aggression were initiated by intermediate crabs (58%). Juvenile and intermediate crabs were most often found in the zone of observation furthest from the water, while more adults were found in the moist zone. Across all age categories, the number of acts of aggression increased in beaches with more activity from outside factors. Age, food availability, and hormonal changes are all possible contributors to aggressive behavior in the G. grapsus

Methane and nitrous oxide from ground-based FTIR at Addis Ababa: Observations, error analysis, and comparison with satellite data

A ground-based, high-spectral-resolution Fourier transform infrared (FTIR) spectrometer has been operational in Addis Ababa, Ethiopia (9.01∘ N latitude, 38.76∘ E longitude; 2443 m altitude above sea level), since May 2009 to obtain information on column abundances and profiles of various constituents in the atmosphere. Vertical profile and column abundances of methane and nitrous oxide are derived from solar absorption measurements taken by FTIR for a period that covers May 2009 to March 2013 using the retrieval code PROFFIT (V9.5). A detailed error analysis of CH$_{4}$ and N$_{2}$O retrieval are performed. Averaging kernels of the target gases shows that the major contribution to the retrieved information comes from the measurement. Thus, average degrees of freedom for signals are found to be 2.1 and 3.4, from the retrieval of CH$_{4}$ and N$_{2}$O for the total observed FTIR spectra. Methane and nitrous oxide volume mixing ratio (VMR) profiles and column amounts retrieved from FTIR spectra are compared with data from the reduced spectral resolution Institute of Meteorology and Climate Research/Instituto de Astrofísica de Andalucía (IMK/IAA) MIPAS (Version V5R_CH4_224 and V5R_N2O_224), the Microwave Limb Sounder (MLS) (MLS v3.3 of N$_{2}$O and CH$_{4}$ derived from MLS v3.3 products of CO, N$_{2}$O, and H$_{2}$O), and the Atmospheric Infrared Sounder (AIRS) sensors on board satellites. The averaged mean relative difference between FTIR methane and the three correlative instruments MIPAS, MLS, and AIRS are 4.2 %, 5.8 %, and 5.3 % in the altitude ranges of 20 to 27 km, respectively. However, the biases below 20 km are negative, which indicates the profile of CH4 from FTIR is less than the profiles derived from correlative instruments by −4.9 %, −1.8 %, and −2.8 %. The averaged positive bias between FTIR nitrous oxide and correlative instrument, MIPAS, in the altitude range of 20 to 27 km is 7.8 %, and a negative bias of −4 % at altitudes below 20 km. An averaged positive bias of 9.3 % in the altitude range of 17 to 27 km is obtained for FTIR N2O with MLS. In all the comparisons of CH$_{4}$ from FTIR with data from MIPAS, MLS, and AIRS, sensors on board satellites indicate a negative bias below 20 km and a positive bias above 20 km. The mean error between partial-column amounts of methane from MIPAS and the ground-based FTIR is −5.5 %, with a standard deviation of 5 % that shows very good agreement as exhibited by relative differences between vertical profiles. Thus, the retrieved CH$_{4}$ and N$_{2}$O VMR and column amounts from Addis Ababa, tropical site, is found to exhibit very good agreement with all coincident satellite observations. Therefore, the bias obtained from the comparison is comparable to the precision of FTIR measurement, which allows the use of data in further scientific studies as it represents a unique environment of tropical Africa, a region poorly investigated in the past