Abstract: The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). Example embodiments of the present disclosure relate to the field of wireless communication technologies, and for example, to a method for determining a timing relationship, including: receiving a timing relationship set indication indicating a timing relationship set for use at a User Equipment (UE); determining the timing relationship set based on the timing relationship set indication; receiving timing relationship indication information indicating a timing relationship for use at the UE; and determining the timing relationship for use at the UE based on the timing relationship set and the timing relationship indication information.

Abstract: The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). The embodiment of the application provides a method for determining the activation state of a secondary cell and a UE. The method is applied to the technical field of wireless communication and comprises the following steps: acquiring secondary cell activation state indicator information, and then determining respective corresponding states of at least one secondary cell configured or reconfigured by a UE based on the secondary cell activation state indicator information, wherein the states include: an activation state and an inactivation state. The embodiment of the application realizes the reduction of the time delay for the user equipment to receive or send data and the reduction of the time required for deactivating a secondary cell, thereby reducing the power consumption of the UE.

Abstract: The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). The present application discloses a power headroom report (PHR) method and apparatus. A user equipment (UE) determines a PHR reporting manner according to the structure of an uplink subframe in an active serving cell, calculates PHR according to the reporting manner, and sends the PHR to a base station. According to the present disclosure, a UE proactively determines the PHR reporting manner, calculates PHR and report the PHR, thus implements PHR reporting process at the UE.

Abstract: A method of mobile automation apparatus localization in a navigation controller includes: controlling a depth sensor to capture a plurality of depth measurements corresponding to an area containing a navigational structure; selecting a primary subset of the depth measurements; selecting, from the primary subset, a corner candidate subset of the depth measurements; generating, from the corner candidate subset, a corner edge corresponding to the navigational structure; selecting an aisle subset of the depth measurements from the primary subset, according to the corner edge; selecting, from the aisle subset, a local minimum depth measurement for each of a plurality of sampling planes extending from the depth sensor; generating a shelf plane from the local minimum depth measurements; and updating a localization of the mobile automation apparatus based on the corner edge and the shelf plane.

Abstract: A method for localization of a mobile automation apparatus includes, at a navigational controller: generating a set of candidate poses within an environmental map; updating the candidate poses according to motion data corresponding to movement of the mobile automation apparatus; receiving observational data collected at an actual pose of the mobile automation apparatus; generating (i) respective weights for the candidate poses, each weight indicating a likelihood that the corresponding candidate pose matches the actual pose, and (ii) a localization confidence level based on the weights; responsive to determining whether the localization confidence level exceeds a candidate pose resampling threshold: when the determination is affirmative, (i) increasing the candidate pose resampling threshold and (ii) generating a further set of candidate poses; and when the determination is negative, (i) decreasing the candidate pose resampling threshold without generating the further set; and repeating the updating, the rece

Abstract: A method for localization of a mobile automation apparatus includes, at a navigational controller: generating a set of candidate poses within an environmental map; updating the candidate poses according to motion data corresponding to movement of the mobile automation apparatus; receiving observational data collected at an actual pose of the mobile automation apparatus; generating (i) respective weights for the candidate poses, each weight indicating a likelihood that the corresponding candidate pose matches the actual pose, and (ii) a localization confidence level based on the weights; responsive to determining whether the localization confidence level exceeds a candidate pose resampling threshold: when the determination is affirmative, (i) increasing the candidate pose resampling threshold and (ii) generating a further set of candidate poses; and when the determination is negative, (i) decreasing the candidate pose resampling threshold without generating the further set; and repeating the updating, the rece

Abstract: A method of mobile automation apparatus localization in a navigation controller includes: controlling a depth sensor to capture a plurality of depth measurements corresponding to an area containing a navigational structure; selecting a primary subset of the depth measurements; selecting, from the primary subset, a corner candidate subset of the depth measurements; generating, from the corner candidate subset, a corner edge corresponding to the navigational structure; selecting an aisle subset of the depth measurements from the primary subset, according to the corner edge; selecting, from the aisle subset, a local minimum depth measurement for each of a plurality of sampling planes extending from the depth sensor; generating a shelf plane from the local minimum depth measurements; and updating a localization of the mobile automation apparatus based on the corner edge and the shelf plane.