An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

Abstract

An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.

Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles

“…Our laser scan-to-map matching method with OLM is based on Maximum Likelihood Estimation (MLE) and aims to find the optimal rigid-body transformation in the m-frame. More details can be seen in our previous work in [40]. The MLE matches current scan S t to a grid-based OLM M t−1 , which is generated by all the previous scans from time step 1 to t−1 and stores the likelihood value of each grid cell in the 3D space region.…”

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

“…Following this trend, several mobile object tracking approaches have recently appeared in literature, considering different aspects of the target issue, such as coverage, completeness, effectiveness, efficiency, etc. The category of algorithms that goes under the name of scanmatching (e.g., [43]- [45]) supports mobile objects positioning in indoor environments based on the acquisition of maps of the environment surrounding the target mobile objects. Maps are acquired from two successive points in the objects' path using a range-scanner sensor positioned on mobile objects themselves.…”

Indoor Mapping Guidance Algorithm of Rotary-Wing UAV Including Dead-End Situations

“…Tang et al [25] proposed a vertex-to-edge weighted loop closure solution to minimize error in full RGB-D indoor SLAM. In addition, Qian et al [26] presented an indoor SLAM algorithm via scan-to-map matching with a two-dimensional laser scanner and Inertial Measurement Unit (IMU).…”

“…The above studies mainly focused on a mapping system [12,14,18,19,21,22,23,24,25], sensor fusion [13,15,26], or solving a SLAM problem [16,17,20]. Furthermore, mobile robots were mostly considered in the above indoor mapping studies.…”