Abstract
Abstract Sensors for environmental perception are nowadays applied in numerous vehicles and are expected to be used in even higher quantities for future autonomous driving. This leads to an increasing amount of observation data that must be processed reliably and accurately very quickly. For this purpose, recursive approaches are particularly suitable in terms of their efficiency when powerful CPUs and GPUs are uneconomical, too large, or too heavy for certain applications. If explicit functional relationships between the available observations and the requested parameters are used to process and adjust the observation data, complementary approaches exist. The situation is different for implicit relationships, which could not be considered recursively for a long time but only in the context of batch adjustments. In this contribution, a recursive Gauss-Helmert model is presented that can handle explicit and implicit equations and thus allows high flexibility. This recursive estimator is based on a Kalman filter for implicit measurement equations, which has already been used for georeferencing kinematic multi-sensor systems (MSS) in urban environments. Furthermore, different methods for introducing additional information using constraints and the resulting added value are shown. Practical application of the methodology is given by an example for the calibration of a laser scanner for a MSS.
Highlights
The usage of high-resolution sensor technologies (such as laser scanners (LS) or cameras) has steadily increasedIncreasingly, the acquired sensor data is processed directly within the framework of adjustment or filtering approaches
A recursive Gauss-Helmert model is presented that can handle explicit and implicit equations and allows high flexibility. This recursive estimator is based on a Kalman filter for implicit measurement equations, which has already been used for georeferencing kinematic multi-sensor systems (MSS) in urban environments
If all acquired data is used within one big adjustment, this batch processing quickly reaches its limits with such vast amounts of data
Summary
The acquired sensor data is processed directly within the framework of adjustment or filtering approaches. If all acquired data is used within one big adjustment, this batch processing quickly reaches its limits with such vast amounts of data Even though such batch methods deliver reliable results, in general, they need to be performed on powerful computers in post-processing [3, 31]. This contradicts the requirements for online approaches G., in the case of autonomous vehicles) Such applications require recursive approaches that process only a certain subset of the data when it is available. For applications where the sensors or an entire MSS moves kinematically within a certain environment over time, a recursive approach is advis-
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