Conference Agenda

Miniaturized UAV laser scanning systems have advanced rapidly over the past decade, especially in the low-cost sector. DJI entered this field with the Zenmuse L-series, integrating GNSS/INS with compact scanners. While the first-generation L1 showed moderate precision, the L2 improved notably through reduced beam divergence. In November 2025, DJI released the Zenmuse L3. In this contribution, we assess its performance.

The main upgrade from L2 to L3 lies in the LiDAR unit: L3 uses a single 1535 nm laser instead of multiple 905 nm diodes, offers a symmetric 0.25 mrad beam divergence, and supports pulse repetition rates from 350 kHz to 2 MHz. High PRR operation is limited to altitudes ≤50 m due to missing multiple-time-around resolution. Scan modes include linear, non-repetitive, and a new star-shaped pattern.

L2 and L3 were tested at three sites in Lower Austria covering a warehouse, power-lines, and forests. Flights were conducted at 80 m AGL (350 kHz) and, for the warehouse, 50 m AGL (2 MHz). Precision, strip consistency, point density, feature separability, and vegetation penetration were evaluated using the scientific software OPALS.

L3 data showed sharper edges, reduced noise, and higher separability, yielding spline-fit residuals of 0.9 cm versus 2.6 cm for L2 for reconstructing a double-threaded power-line. Ground point coverage in forests increased from 18 % (L2) to 51 % (L3). Strip height differences are around 2 cm for both sensors and L3 achieved sub-centimeter precision on sealed surfaces. Overall, L3 offers substantial gains in spatial resolution, precision, and vegetation penetration.

Intrinsic calibration of rotating multi-beam lidars (RMBL) enables more precise measurements. We calibrated our sensor to improve its geometric and radiometric accuracy using a rotating tilted platform. The rotating mechanism widens the field of view of each lidar channel and allows all lasers of the sensor to measure the same areas in a room containing planar wall and floor sections. Therefore, we can collect measurements for geometric and radiometric calibration with minimal amount of calibration targets. Furthermore, we used data based numerical minimization to estimate the calibration parameters for all 128 lidar channels in our RMBL sensor. For the intrinsic geometric calibration of the sensor, we estimated the elevation and azimuth angles of each laser. For the radiometry, we estimated a linear model for each laser to correct the intensity measurement. For a linear model, two different known diffuse reflectance targets are sufficient for the radiometric calibration. We tested our methods in two different environments, in an office room and a longer corridor. We showed that the methods can improve the precision of the RMBL sensor significantly. Regarding geometry, we were able to reduce the error on average from 16.1 mm to 15.1 mm (6.2% improvement). For radiometry, we were able to improve the reflectance measuring accuracy on average from 9.5% errors down to -0.9% errors (91% improvement).

In recent years, laser scanning has evolved into a core surveying technology for 3D mapping, both statically from stationary scan positions (terrestrial laser scanning, TLS) and kinematically from moving platforms (kinematic laser scanning, KLS). Consequently, there is a growing demand for methods that efficiently and coherently support both static and kinematic data acquisition modes. This contribution presents a tightly-coupled approach for the co-registration of TLS and KLS data, which simultaneously integrates GNSS positions, inertial measurements, planar features extracted from both static and kinematic point clouds, and control information in a joint non-linear least-squares adjustment. This is neither just a transformation of the kinematic onto the static point cloud nor a simple correction of the trajectory in e.g., a strip adjustment, but rather a tightly coupled adjustment of static and kinematic data. This approach avoids the need for additional survey control for kinematic data by leveraging the static scan data as a proxy, enabling accurate georeferencing even in scenarios where the individual datasets cannot be reliably tied to control points. Results show that the co-registration notably improves the relative consistency of kinematic datasets with respect to a static reference. Such co-registration enables new use-cases for multi-modal data acquisition, such as change-detection in repeated kinematic data acquisitions with respect to a static reference dataset, or more flexible ways of integrating ground control in kinematic surveys.

This study investigates the use of a distributed asynchronous lidar system for augmented position and orientation determination in Global Navigation Satellite Systems (GNSS) denied environments. An asynchronous lidar design is one in which the laser transmitter and detectors/receivers are disconnected and carried on separate platforms. This unique geometry offers observational redundancy that can be used to estimate the trajectory of the receiver platforms. The paper presents the results of simulation experiments, first examining single epoch solutions and then considers estimates of position and orientation along simulated flight trajectories. The results show that as long as the laser transmitter is operated above the GNSS denied environment, the system is able to simultaneously estimate position and orientation for multiple receiver drones, even for extended periods of GNSS outages. The accuracy of position and orientation estimation is dependent on the exact flight path and the number of lidar receivers in the solution, but with favorable geometry the accuracy of position estimation can approach that provided by a high precision GNSS solution.

This work extends our initial proof-of-concept via emulations on the benefits of relative spatial constraints between imagery and lidar point clouds in a factor graph based optimization with satellite positioning (GNSS) and raw inertial readings (Mouzakidou et al., 2025). Here, we demonstrate practically the automatic extraction and integration of 2D-3D correspondences established in the 3D domain within rough natural terrain flown over by an aircraft with sensors of high quality. We show that considering cross-domain (i.e. 2D-3D) constraints enables the calibration of internal camera parameters and its boresight on job, i.e. within mapping flight configurations, where conventional approaches fail. The common optimization of raw IMU data with such constraints improves the respective agreements between the lidar and image dense clouds, achieving consistency at ground resolution level, which is not the case for the conventional (standard) processing of acquired data.

Visual-inertial odometry (VIO) plays a key role in modern navigation and mapping systems.

For their successful integration, an initialization phase, in which IMU-related bias factors are estimated, becomes a fundamental step.

Without one, the subsequent nonlinear estimation of the platform pose may fail to converge or completely diverge.

As reliance on visual and inertial information may exhibit instability due to error accumulation with time, incorporating absolute positioning information through global navigation satellite system (GNSS) measurements, may enhance its robustness and accuracy.

Accordingly, GNSS and visual-inertial initialization frameworks have been receiving growing attention in recent years where current strategies tend to follow a loosely-coupled formulation that first initializes the VIO trajectory, and then aligns it with GNSS measurements.

Such strategies are multi-stage, nonlinear, and computationally expensive, motivating us to introduce an alternative framework in which GNSS position is integrated with the raw visual-inertial measurements to form absolute translation constraints.

Accordingly, we achieve a closed-form, linear and globally consistent drift-free solution which is computationally efficient and requires neither 3D reconstruction nor nonlinear refinement, as common approaches do.

Testing our initialization formulation on benchmark multi-sensor datasets, results show that we outperform current baselines while exhibiting robustness in challenging scenarios.