LiDAR Enhanced Structure-from-Motion

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.Comment: 6 pages plus reference. Work has been submitted to ICRA 202

RadarSLAM: Radar based Large-Scale SLAM in All Weathers

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations

CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR

We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time

The High Arctic in Extreme Winters: Vortex, Temperature, and MLS and ACE-FTS Trace Gas Evolution

The first three Canadian Arctic Atmospheric Chemistry Experiment (ACE) Validation Campaigns at Eureka (80° N, 86° W) were during two extremes of Arctic winter variability: Stratospheric sudden warmings (SSWs) in 2004 and 2006 were among the strongest, most prolonged on record; 2005 was a record cold winter. New satellite measurements from ACE-Fourier Transform Spectrometer (ACE-FTS), Sounding of the Atmosphere using Broadband Emission Radiometry, and Aura Microwave Limb Sounder (MLS), with meteorological analyses and Eureka lidar and radiosonde temperatures, are used to detail the meteorology in these winters, to demonstrate its influence on transport and chemistry, and to provide a context for interpretation of campaign observations. During the 2004 and 2006 SSWs, the vortex broke down throughout the stratosphere, reformed quickly in the upper stratosphere, and remained weak in the middle and lower stratosphere. The stratopause reformed at very high altitude, above where it could be accurately represented in the meteorological analyses. The 2004 and 2006 Eureka campaigns were during the recovery from the SSWs, with the redeveloping vortex over Eureka. 2005 was the coldest winter on record in the lower stratosphere, but with an early final warming in mid-March. The vortex was over Eureka at the start of the 2005 campaign, but moved away as it broke up. Disparate temperature profile structure and vortex evolution resulted in much lower (higher) temperatures in the upper (lower) stratosphere in 2004 and 2006 than in 2005. Satellite temperatures agree well with Eureka radiosondes, and with lidar data up to 50–60 km. Consistent with a strong, cold upper stratospheric vortex and enhanced radiative cooling after the SSWs, MLS and ACE-FTS trace gas measurements show strongly enhanced descent in the upper stratospheric vortex during the 2004 and 2006 Eureka campaigns compared to that in 2005