Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

Abstract

We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data.

Single Photon Lidar in Mobile Laser Scanning: The Sampling Rate Problem and Initial Solutions via Spatial Correlations

“…This original photon arrival distribution is typically Poissonian, and can be estimated from the observations as a post-processing step (Pediredla et al, 2018). Here, however, we seek to shortcut the (histogram) sampling phase by relying in spatial correlations (Lehtola et al, 2019). This hopefully leads onto a more computationally efficient methodology that could be used to pre-process the data on-chip (e.g.…”

“…We study whether the use of spatial correlations can help to distinguish background illumination from the true signal. For this, we adapt the methodology presented in (Lehtola et al, 2019) and previously in (Lehtola et al, 2016b) as follows. The singlephoton solid-state lidar observations consist of range measurements from single photons…”

“…The support technique can deal with noise but it has its limits. In (Lehtola et al, 2019), limited target detection (tree trunk points) is plausible even when these measurements consist of 5% of the total data. Here, as shown in Figure 2, the observations from the target get drowned in the noise as they consist only of about 0.18 % of the data (ratio between the area of red exp-normalized peak and the area of the black curve).…”

“…For these reasons, the filtering of abundant outliers before SLAM (i.e. registration) has been studied in (Lehtola et al, 2016b, Lehtola et al, 2016a, and in more general form addressing spatial correlations in (Lehtola et al, 2019). However, these works do not utilize a single photon lidar.…”

“…techniques for outlier filtering before data registration from (Lehtola et al, 2019). We shall study data captured during daylight and from targets illuminated by direct sunlight.…”